Your Name
8661 lines
354 KiB
Plaintext
8661 lines
354 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "1UdZgdMa6L9X"
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},
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "xieWpvmi6L9Z"
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},
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"source": [
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"### News"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "T5zA3wvi6L9Z"
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},
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"source": [
|
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"### Installation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "yxLJvyfz6L9Z"
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},
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"outputs": [],
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"source": [
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"%%capture\n",
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"import os, re\n",
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"if \"COLAB_\" not in \"\".join(os.environ.keys()):\n",
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" !pip install unsloth\n",
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"else:\n",
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" # Do this only in Colab notebooks! Otherwise use pip install unsloth\n",
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" import torch; v = re.match(r\"[0-9\\.]{3,}\", str(torch.__version__)).group(0)\n",
|
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" xformers = \"xformers==\" + (\"0.0.32.post2\" if v == \"2.8.0\" else \"0.0.29.post3\")\n",
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" !pip install --no-deps bitsandbytes accelerate {xformers} peft trl triton cut_cross_entropy\n",
|
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" !pip install sentencepiece protobuf \"datasets>=3.4.1,<4.0.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n",
|
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" !pip install \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\"\n",
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" !pip install \"unsloth[base] @ git+https://github.com/unslothai/unsloth\"\n",
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"!pip install transformers==4.55.4"
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]
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},
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{
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||
"cell_type": "markdown",
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"metadata": {
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||
"id": "hSZDfEpp6L9a"
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},
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"source": [
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"### Unsloth"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 880,
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]
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},
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||
"id": "QmUBVEnvCDJv",
|
||
"outputId": "833593bc-378f-4726-ac6f-8bf57b0ee091"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
|
||
"🦥 Unsloth Zoo will now patch everything to make training faster!\n",
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||
"==((====))== Unsloth 2025.8.10: Fast Llama patching. Transformers: 4.55.4.\n",
|
||
" \\\\ /| NVIDIA A100-SXM4-80GB. Num GPUs = 1. Max memory: 79.138 GB. Platform: Linux.\n",
|
||
"O^O/ \\_/ \\ Torch: 2.8.0+cu128. CUDA: 8.0. CUDA Toolkit: 12.8. Triton: 3.4.0\n",
|
||
"\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.32.post2. FA2 = False]\n",
|
||
" \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
|
||
"Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "c72a6c31a1e7490b9d897068a89442ed",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Loading checkpoint shards: 0%| | 0/8 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from unsloth import FastLanguageModel\n",
|
||
"import torch\n",
|
||
"max_seq_length = 48000 # Choose any! We auto support RoPE Scaling internally!\n",
|
||
"dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
|
||
"load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
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"\n",
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||
"# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
|
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"fourbit_models = [\n",
|
||
" \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\", # Llama-3.1 2x faster\n",
|
||
" \"unsloth/Meta-Llama-3.1-70B-bnb-4bit\",\n",
|
||
" \"unsloth/Mistral-Small-Instruct-2409\", # Mistral 22b 2x faster!\n",
|
||
" \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
|
||
" \"unsloth/Phi-3.5-mini-instruct\", # Phi-3.5 2x faster!\n",
|
||
" \"unsloth/Phi-3-medium-4k-instruct\",\n",
|
||
" \"unsloth/gemma-2-27b-bnb-4bit\", # Gemma 2x faster!\n",
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||
"\n",
|
||
" \"unsloth/Llama-3.2-1B-bnb-4bit\", # NEW! Llama 3.2 models\n",
|
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" \"unsloth/Llama-3.2-1B-Instruct-bnb-4bit\",\n",
|
||
" \"unsloth/Llama-3.2-3B-Instruct-bnb-4bit\",\n",
|
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"] # More models at https://huggingface.co/unsloth\n",
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"\n",
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"qwen_models = [\n",
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" \"unsloth/Qwen2.5-Coder-32B-Instruct\", # Qwen 2.5 Coder 2x faster\n",
|
||
" \"unsloth/Qwen2.5-Coder-7B\",\n",
|
||
" \"unsloth/Qwen2.5-14B-Instruct\", # 14B fits in a 16GB card\n",
|
||
" \"unsloth/Qwen2.5-7B\",\n",
|
||
" \"unsloth/Qwen2.5-72B-Instruct\", # 72B fits in a 48GB card\n",
|
||
"] # More models at https://huggingface.co/unsloth\n",
|
||
"\n",
|
||
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
|
||
" model_name = \"unsloth/llama-3.3-70b-instruct-bnb-4bit\",\n",
|
||
" max_seq_length = max_seq_length,\n",
|
||
" dtype = dtype,\n",
|
||
" load_in_4bit = load_in_4bit,\n",
|
||
" # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "SXd9bTZd1aaL"
|
||
},
|
||
"source": [
|
||
"We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import gc, torch\n",
|
||
"gc.collect()\n",
|
||
"torch.cuda.empty_cache()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Wed Aug 27 21:27:00 2025 \n",
|
||
"+-----------------------------------------------------------------------------------------+\n",
|
||
"| NVIDIA-SMI 565.57.01 Driver Version: 565.57.01 CUDA Version: 12.8 |\n",
|
||
"|-----------------------------------------+------------------------+----------------------+\n",
|
||
"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
|
||
"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
|
||
"| | | MIG M. |\n",
|
||
"|=========================================+========================+======================|\n",
|
||
"| 0 NVIDIA A100-SXM4-80GB On | 00000000:87:00.0 Off | 0 |\n",
|
||
"| N/A 30C P0 61W / 400W | 1MiB / 81920MiB | 0% Default |\n",
|
||
"| | | Disabled |\n",
|
||
"+-----------------------------------------+------------------------+----------------------+\n",
|
||
" \n",
|
||
"+-----------------------------------------------------------------------------------------+\n",
|
||
"| Processes: |\n",
|
||
"| GPU GI CI PID Type Process name GPU Memory |\n",
|
||
"| ID ID Usage |\n",
|
||
"|=========================================================================================|\n",
|
||
"| No running processes found |\n",
|
||
"+-----------------------------------------------------------------------------------------+\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!nvidia-smi"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "6bZsfBuZDeCL",
|
||
"outputId": "c1db06c7-7b3a-4a8e-ae75-2c04d0dacf4b"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Unsloth 2025.8.10 patched 80 layers with 80 QKV layers, 80 O layers and 80 MLP layers.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model = FastLanguageModel.get_peft_model(\n",
|
||
" model,\n",
|
||
" r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
|
||
" target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
|
||
" \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
|
||
" lora_alpha = 16,\n",
|
||
" lora_dropout = 0, # Supports any, but = 0 is optimized\n",
|
||
" bias = \"none\", # Supports any, but = \"none\" is optimized\n",
|
||
" # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
|
||
" use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
|
||
" random_state = 3407,\n",
|
||
" use_rslora = False, # We support rank stabilized LoRA\n",
|
||
" loftq_config = None, # And LoftQ\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "vITh0KVJ10qX"
|
||
},
|
||
"source": [
|
||
"<a name=\"Data\"></a>\n",
|
||
"### Data Prep\n",
|
||
"We now use the `Qwen-2.5` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. But we convert it to HuggingFace's normal multiturn format `(\"role\", \"content\")` instead of `(\"from\", \"value\")`/ Qwen renders multi turn conversations like below:\n",
|
||
"\n",
|
||
"```\n",
|
||
"<|im_start|>system\n",
|
||
"You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n",
|
||
"<|im_start|>user\n",
|
||
"What is 2+2?<|im_end|>\n",
|
||
"<|im_start|>assistant\n",
|
||
"It's 4.<|im_end|>\n",
|
||
"\n",
|
||
"```\n",
|
||
"\n",
|
||
"We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3` and more."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loaded 10000 examples\n",
|
||
"Dataset({\n",
|
||
" features: ['conversation', 'metadata', 'original_example'],\n",
|
||
" num_rows: 10000\n",
|
||
"})\n",
|
||
"{'conversation': [{'content': 'You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.', 'role': 'system'}, {'content': 'I\\'m having an issue with the DataDog/integrations-core codebase. Here\\'s the problem:\\n\\nElasticsearch check fails when used with Opensearch\\n**Note:** If you have a feature request, you should [contact support](https://docs.datadoghq.com/help/) so the request can be properly tracked.\\r\\n\\r\\n**Output of the [info page](https://docs.datadoghq.com/agent/guide/agent-commands/#agent-status-and-information)**\\r\\n\\r\\n```text\\r\\n elastic (1.24.0)\\r\\n ----------------\\r\\n Instance ID: elastic:e6126c6cf9b8eebe [ERROR]\\r\\n Configuration Source: file:/etc/datadog-agent/conf.d/elastic.yaml\\r\\n Total Runs: 121\\r\\n Metric Samples: Last Run: 0, Total: 0\\r\\n Events: Last Run: 0, Total: 0\\r\\n Service Checks: Last Run: 1, Total: 121\\r\\n Average Execution Time : 13ms\\r\\n Last Execution Date : 2021-09-10 06:00:16 UTC (1631253616000)\\r\\n Last Successful Execution Date : Never\\r\\n metadata:\\r\\n version.major: 1\\r\\n version.minor: 0\\r\\n version.patch: 0\\r\\n version.raw: 1.0.0\\r\\n version.scheme: semver\\r\\n Error: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n Traceback (most recent call last):\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\", line 897, in run\\r\\n self.check(instance)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 79, in check\\r\\n stats_data = self._get_data(stats_url)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 232, in _get_data\\r\\n resp.raise_for_status()\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\", line 940, in raise_for_status\\r\\n raise HTTPError(http_error_msg, response=self)\\r\\n requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n\\r\\n```\\r\\n\\r\\n**Additional environment details (Operating System, Cloud provider, etc):**\\r\\nFedora 33 running agent inside systemd-nspawn container. Container is running Opensearch 1.0.0 and datadog agent has been configured to monitor it.\\r\\n\\r\\n**Steps to reproduce the issue:**\\r\\n1. Install and run Opensearch\\r\\n2. Configure datadog agent Elasticsearch integration to monitor it\\r\\n3. Observe that the Elasticsearch check is failing\\r\\n\\r\\n**Describe the results you received:**\\r\\n\\r\\nThe Elasticsearch check fails in the following way:\\r\\n```text\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:261 in work) | check:elastic | Running check\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/check.go:81 in runCheck) | Running python check elastic elastic:878a21a7a9cbcd49\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET / HTTP/1.1\" 200 349\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:247) | request to url http://localhost:9200 returned: <Response [200]>\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:147) | Elasticsearch version is [1, 0, 0]\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET /_nodes/_local/stats?all=true HTTP/1.1\" 400 155\\r\\n2021-09-09 12:48:49 UTC | CORE | ERROR | (pkg/collector/runner/runner.go:292 in work) | Error running check elastic: [{\"message\": \"400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\", \"traceback\": \"Traceback (most recent call last):\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\\\\\", line 897, in run\\\\n self.check(instance)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 79, in check\\\\n stats_data = self._get_data(stats_url)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 232, in _get_data\\\\n resp.raise_for_status()\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\\\\\", line 940, in raise_for_status\\\\n raise HTTPError(http_error_msg, response=self)\\\\nrequests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\\\n\"}]\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:327 in work) | check:elastic | Done running check\\r\\n```\\r\\n\\r\\n**Describe the results you expected:**\\r\\n\\r\\nOpensearch 1.0.0 is fully compatible with Elasticsearch 7.10.2 and datadog agent should be able to monitor it in the same way as the Elasticsearch.\\r\\n\\r\\n**Additional information you deem important (e.g. issue happens only occasionally):**\\r\\n\\r\\nThe issue seems to be that since Opensearch is reporting its version as `1.0.0` the Elasticsearch check interprets it as Elasticsearch `1.0.0` and for this reason adds `?all=true` to the URL when calling `/_nodes/_local/stats` API to collect the node stats. https://github.com/DataDog/integrations-core/blob/40d9ceb14c57c109e8f6371b1a4c677fa33e1669/elastic/datadog_checks/elastic/elastic.py#L215\\r\\n\\r\\nThe `?all=true` parameter has been depricated since Elasticsearch version `5.0.0` and results in HTTP 400 error when used with newer versions of Elasticsearch. All is working well when Elasticsearch check is monitoring Opensearch if `?all=true` has been left out when making requests to `/_nodes/_local/stats`.\\r\\n\\r\\nThe version information returned from Opensearch contains field called `distribution` which has value `opensearch` that could be used to detect that the check is actually running against an Opensearch server. Example here:\\r\\n```text\\r\\n{\\'name\\': \\'os-355e47a7-1\\', \\'cluster_name\\': \\'fc7a706a-9c51-40a1-9e1b-953d1546caa0\\', \\'cluster_uuid\\': \\'HrCNW5uhSZKtHxlJQz444g\\', \\'version\\': {\\'distribution\\': \\'opensearch\\', \\'number\\': \\'1.0.0\\', \\'build_type\\': \\'unknown\\', \\'build_hash\\': \\'unknown\\', \\'build_date\\': \\'2021-08-13T12:38:05.881264Z\\', \\'build_snapshot\\': False, \\'lucene_version\\': \\'8.8.2\\', \\'minimum_wire_compatibility_version\\': \\'6.8.0\\', \\'minimum_index_compatibility_version\\': \\'6.0.0-beta1\\'}, \\'tagline\\': \\'The OpenSearch Project: https://opensearch.org/\\'}\\r\\n```\\n\\n\\nCan you help me debug this systematically and explain your reasoning?', 'role': 'user'}, {'content': '', 'role': 'assistant'}], 'metadata': {'base_commit': '160cfef6e1118061fa66d333e8c2a572f5d0a815', 'dataset': 'princeton-nlp/SWE-bench', 'generation_timestamp': '2025-08-19T23:35:38.773997', 'has_patch': True, 'instance_id': 'DataDog__integrations-core-10093', 'original_index': 0, 'reasoning_length': 0, 'repo': 'DataDog/integrations-core', 'source': 'swe-bench'}, 'original_example': {'instance_id': 'DataDog__integrations-core-10093', 'patch': \"diff --git a/elastic/datadog_checks/elastic/elastic.py b/elastic/datadog_checks/elastic/elastic.py\\n--- a/elastic/datadog_checks/elastic/elastic.py\\n+++ b/elastic/datadog_checks/elastic/elastic.py\\n@@ -139,11 +139,17 @@ def _get_es_version(self):\\n try:\\n data = self._get_data(self._config.url, send_sc=False)\\n raw_version = data['version']['number']\\n+\\n self.set_metadata('version', raw_version)\\n # pre-release versions of elasticearch are suffixed with -rcX etc..\\n # peel that off so that the map below doesn't error out\\n raw_version = raw_version.split('-')[0]\\n version = [int(p) for p in raw_version.split('.')[0:3]]\\n+ if data['version'].get('distribution', '') == 'opensearch':\\n+ # Opensearch API is backwards compatible with ES 7.10.0\\n+ # https://opensearch.org/faq\\n+ self.log.debug('OpenSearch version %s detected', version)\\n+ version = [7, 10, 0]\\n except AuthenticationError:\\n raise\\n except Exception as e:\\n\", 'problem_statement': 'Elasticsearch check fails when used with Opensearch\\n**Note:** If you have a feature request, you should [contact support](https://docs.datadoghq.com/help/) so the request can be properly tracked.\\r\\n\\r\\n**Output of the [info page](https://docs.datadoghq.com/agent/guide/agent-commands/#agent-status-and-information)**\\r\\n\\r\\n```text\\r\\n elastic (1.24.0)\\r\\n ----------------\\r\\n Instance ID: elastic:e6126c6cf9b8eebe [ERROR]\\r\\n Configuration Source: file:/etc/datadog-agent/conf.d/elastic.yaml\\r\\n Total Runs: 121\\r\\n Metric Samples: Last Run: 0, Total: 0\\r\\n Events: Last Run: 0, Total: 0\\r\\n Service Checks: Last Run: 1, Total: 121\\r\\n Average Execution Time : 13ms\\r\\n Last Execution Date : 2021-09-10 06:00:16 UTC (1631253616000)\\r\\n Last Successful Execution Date : Never\\r\\n metadata:\\r\\n version.major: 1\\r\\n version.minor: 0\\r\\n version.patch: 0\\r\\n version.raw: 1.0.0\\r\\n version.scheme: semver\\r\\n Error: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n Traceback (most recent call last):\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\", line 897, in run\\r\\n self.check(instance)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 79, in check\\r\\n stats_data = self._get_data(stats_url)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 232, in _get_data\\r\\n resp.raise_for_status()\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\", line 940, in raise_for_status\\r\\n raise HTTPError(http_error_msg, response=self)\\r\\n requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n\\r\\n```\\r\\n\\r\\n**Additional environment details (Operating System, Cloud provider, etc):**\\r\\nFedora 33 running agent inside systemd-nspawn container. Container is running Opensearch 1.0.0 and datadog agent has been configured to monitor it.\\r\\n\\r\\n**Steps to reproduce the issue:**\\r\\n1. Install and run Opensearch\\r\\n2. Configure datadog agent Elasticsearch integration to monitor it\\r\\n3. Observe that the Elasticsearch check is failing\\r\\n\\r\\n**Describe the results you received:**\\r\\n\\r\\nThe Elasticsearch check fails in the following way:\\r\\n```text\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:261 in work) | check:elastic | Running check\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/check.go:81 in runCheck) | Running python check elastic elastic:878a21a7a9cbcd49\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET / HTTP/1.1\" 200 349\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:247) | request to url http://localhost:9200 returned: <Response [200]>\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:147) | Elasticsearch version is [1, 0, 0]\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET /_nodes/_local/stats?all=true HTTP/1.1\" 400 155\\r\\n2021-09-09 12:48:49 UTC | CORE | ERROR | (pkg/collector/runner/runner.go:292 in work) | Error running check elastic: [{\"message\": \"400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\", \"traceback\": \"Traceback (most recent call last):\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\\\\\", line 897, in run\\\\n self.check(instance)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 79, in check\\\\n stats_data = self._get_data(stats_url)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 232, in _get_data\\\\n resp.raise_for_status()\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\\\\\", line 940, in raise_for_status\\\\n raise HTTPError(http_error_msg, response=self)\\\\nrequests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\\\n\"}]\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:327 in work) | check:elastic | Done running check\\r\\n```\\r\\n\\r\\n**Describe the results you expected:**\\r\\n\\r\\nOpensearch 1.0.0 is fully compatible with Elasticsearch 7.10.2 and datadog agent should be able to monitor it in the same way as the Elasticsearch.\\r\\n\\r\\n**Additional information you deem important (e.g. issue happens only occasionally):**\\r\\n\\r\\nThe issue seems to be that since Opensearch is reporting its version as `1.0.0` the Elasticsearch check interprets it as Elasticsearch `1.0.0` and for this reason adds `?all=true` to the URL when calling `/_nodes/_local/stats` API to collect the node stats. https://github.com/DataDog/integrations-core/blob/40d9ceb14c57c109e8f6371b1a4c677fa33e1669/elastic/datadog_checks/elastic/elastic.py#L215\\r\\n\\r\\nThe `?all=true` parameter has been depricated since Elasticsearch version `5.0.0` and results in HTTP 400 error when used with newer versions of Elasticsearch. All is working well when Elasticsearch check is monitoring Opensearch if `?all=true` has been left out when making requests to `/_nodes/_local/stats`.\\r\\n\\r\\nThe version information returned from Opensearch contains field called `distribution` which has value `opensearch` that could be used to detect that the check is actually running against an Opensearch server. Example here:\\r\\n```text\\r\\n{\\'name\\': \\'os-355e47a7-1\\', \\'cluster_name\\': \\'fc7a706a-9c51-40a1-9e1b-953d1546caa0\\', \\'cluster_uuid\\': \\'HrCNW5uhSZKtHxlJQz444g\\', \\'version\\': {\\'distribution\\': \\'opensearch\\', \\'number\\': \\'1.0.0\\', \\'build_type\\': \\'unknown\\', \\'build_hash\\': \\'unknown\\', \\'build_date\\': \\'2021-08-13T12:38:05.881264Z\\', \\'build_snapshot\\': False, \\'lucene_version\\': \\'8.8.2\\', \\'minimum_wire_compatibility_version\\': \\'6.8.0\\', \\'minimum_index_compatibility_version\\': \\'6.0.0-beta1\\'}, \\'tagline\\': \\'The OpenSearch Project: https://opensearch.org/\\'}\\r\\n```\\n', 'repo': 'DataDog/integrations-core'}}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from datasets import Dataset\n",
|
||
"import json\n",
|
||
"\n",
|
||
"file_path = \"../data/raw/swe_reasoning_dataset (3).jsonl\"\n",
|
||
"\n",
|
||
"# Load JSONL\n",
|
||
"data = []\n",
|
||
"with open(file_path, \"r\", encoding=\"utf-8\") as f:\n",
|
||
" for line in f:\n",
|
||
" data.append(json.loads(line))\n",
|
||
"\n",
|
||
"print(f\"Loaded {len(data)} examples\")\n",
|
||
"\n",
|
||
"# Convert to HuggingFace Dataset\n",
|
||
"dataset = Dataset.from_list(data)\n",
|
||
"\n",
|
||
"print(dataset)\n",
|
||
"print(dataset[0]) # Show first example\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 141,
|
||
"referenced_widgets": [
|
||
"e7371eff6f4c458792abd1e05e147110",
|
||
"b27f191668cf4d1cbf9705fe51e3c3df",
|
||
"c19fa4dea6524d37843cbf5786b26221",
|
||
"594a5e73c6d24e8a8f98400c2ee99636",
|
||
"709b591f878747ee9a22c01bb8fa2c74",
|
||
"6e2018997fc545d8bb37c862fff0c751",
|
||
"acda49009d124138843b187c2aa9e410",
|
||
"fba4df061ec84f4abe521ee628c53b62",
|
||
"e0cf51730c35468ea3074be96df8b8a0",
|
||
"38db9cffac4a42759c3c1be2e5a4dd3f",
|
||
"32b193ccdb024d5993e42b4e0efd1631",
|
||
"f4a36fec7d7a41e49bf125cc5cc98b0f",
|
||
"a2951310cc79437a8dac88cadd9ed1e6",
|
||
"8cf481de1931406e97951a2adfbdeee0",
|
||
"24f12af7e8664d72ae38b06a713294a5",
|
||
"0e66ab8fb2384f09a303a034fbde6247",
|
||
"254584c067e146809ab71a7a85d7c7a4",
|
||
"44fdce605bf54fba80f81524461259bf",
|
||
"101d6d98e8bb4a1ab216ddac084314ec",
|
||
"617973da3a3f4b1f86729365d12f9875",
|
||
"a7d2b0d1a685407fa0633eb56f3676db",
|
||
"7dbc6c270eff43569640b25e450601ab",
|
||
"6765cb1a549f45e998ccb19139e2e8a3",
|
||
"c2d9520cf1134cc182c2612bfd912875",
|
||
"a53fab9c72f6468aaffd5bab3765af49",
|
||
"244e4cf841cd483b8e9e165c77c471f0",
|
||
"8bbeb59a93de469682466e54bc6c35a7",
|
||
"0df17c7f00c949c7b8444b91754fa4dd",
|
||
"a8f4a39bb9d74a9bb4138c8bed4186e5",
|
||
"8fe160b637b24a74a4a97bdd9b1858d1",
|
||
"57c9f679f77241d0b6f9bf794dad2b9b",
|
||
"b47587f1ba944ad9850fcf555cdbcf12",
|
||
"82806a720dad4704ba224812d9018bc2"
|
||
]
|
||
},
|
||
"id": "LjY75GoYUCB8",
|
||
"outputId": "5cf90520-4c68-4844-c050-b72a075f3ee6"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from unsloth.chat_templates import get_chat_template\n",
|
||
"\n",
|
||
"tokenizer = get_chat_template(\n",
|
||
" tokenizer,\n",
|
||
" chat_template = \"qwen-2.5\",\n",
|
||
")\n",
|
||
"\n",
|
||
"def formatting_prompts_func(examples):\n",
|
||
" convos = examples[\"conversation\"]\n",
|
||
" texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n",
|
||
" return { \"text\" : texts, }\n",
|
||
"pass\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "K9CBpiISFa6C"
|
||
},
|
||
"source": [
|
||
"We now use `standardize_sharegpt` to convert ShareGPT style datasets into HuggingFace's generic format. This changes the dataset from looking like:\n",
|
||
"```\n",
|
||
"{\"from\": \"system\", \"value\": \"You are an assistant\"}\n",
|
||
"{\"from\": \"human\", \"value\": \"What is 2+2?\"}\n",
|
||
"{\"from\": \"gpt\", \"value\": \"It's 4.\"}\n",
|
||
"```\n",
|
||
"to\n",
|
||
"```\n",
|
||
"{\"role\": \"system\", \"content\": \"You are an assistant\"}\n",
|
||
"{\"role\": \"user\", \"content\": \"What is 2+2?\"}\n",
|
||
"{\"role\": \"assistant\", \"content\": \"It's 4.\"}\n",
|
||
"```"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 157,
|
||
"referenced_widgets": [
|
||
"dee9866d465e43c99e61f71b819d9994",
|
||
"ba0b2bd35ce44100be27bc749642e1cc",
|
||
"b83dcf4897f544678d85a072cfc8bcca",
|
||
"3215fa6f5c0446b9b6b4a0d15dd33032",
|
||
"79ce9732a3af4920a64598318a491398",
|
||
"16946595a9064832a233c889e01818c9",
|
||
"ebdb9c8af7be4743b2e3c1308bddc0a5",
|
||
"a6baf9fd477342fe8474022ac8e7a6ed",
|
||
"3371554f74674d2da66fc8c794822c24",
|
||
"bccfa4e6adfd40d3afff51f5ab038422",
|
||
"d6292ba7c64e4e3f9baf68905657ec56",
|
||
"ffcb4731dff04faa8323dceae528bce3",
|
||
"8bcfc8c1bd6c474fabac2dfff5ca829d",
|
||
"b608a884c04541ca88253a72d1d81cb6",
|
||
"975b0754e56546d4bd7103b4f28a37c5",
|
||
"3ae2a86a77a34f61a204bd1355dda8b3",
|
||
"23cf64d2dff94432af9cf6a7f99744ae",
|
||
"bfef7ae67a7b43ed816246be0ed19a79",
|
||
"760c42f3b2054820acf6b1b6da11356a",
|
||
"cf74cf0282cd409498112783ef3068aa",
|
||
"e38e2b0f246d4b369487fd405be38e15",
|
||
"6b4a5dee5e3143a9b69dede345b7187d"
|
||
]
|
||
},
|
||
"id": "oPXzJZzHEgXe",
|
||
"outputId": "a1d12c39-303d-4ffc-83e9-3e4dd35cf172"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "73320a4b91fe4c61af5bb1225e050f05",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Map: 0%| | 0/10000 [00:00<?, ? examples/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from unsloth.chat_templates import standardize_sharegpt\n",
|
||
"dataset = standardize_sharegpt(dataset)\n",
|
||
"dataset = dataset.map(formatting_prompts_func, batched = True,)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ndDUB23CGAC5"
|
||
},
|
||
"source": [
|
||
"We look at how the conversations are structured for item 5:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "gGFzmplrEy9I",
|
||
"outputId": "aeafe83e-9e67-40fe-8a32-c5bf151f8093"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[{'content': 'You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.',\n",
|
||
" 'role': 'system'},\n",
|
||
" {'content': \"I'm having an issue with the DataDog/integrations-core codebase. Here's the problem:\\n\\n[apache] fix metric collection\\n### What does this PR do?\\r\\n\\r\\nThis collects the correct data for `apache.net.bytes_per_s` and `apache.net.request_per_s`.\\r\\n\\r\\nhttps://www.datadoghq.com/blog/collect-apache-performance-metrics\\r\\nhttps://wiki.opennms.org/wiki/Monitoring_Apache_with_the_HTTP_collector\\r\\nhttps://httpd.apache.org/docs/current/mod/mod_status.html\\r\\n\\r\\n### Motivation\\r\\n\\r\\nCustomer encountered bug\\n\\n\\nCan you help me debug this systematically and explain your reasoning?\",\n",
|
||
" 'role': 'user'},\n",
|
||
" {'content': '## INITIAL PROBLEM ASSESSMENT\\n\\nThe bug report indicates that there was an issue with metric collection for Apache performance metrics, specifically `apache.net.bytes_per_s` and `apache.net.request_per_s`. This suggests that the application was not reporting accurate or expected values for these metrics, which could lead to misinterpretation of server performance by users relying on this data.\\n\\nSymptoms might include:\\n- Users not seeing expected values in monitoring dashboards.\\n- Discrepancies in data reported over time, potentially leading to false conclusions about server load and performance.\\n- Customer complaints about the monitoring system\\'s reliability.\\n\\n## HYPOTHESIS FORMATION\\n\\n1. **Incorrect Metric Mapping**: The metrics `apache.net.bytes_per_s` and `apache.net.request_per_s` might not be correctly mapped in the code, leading to values not being collected or reported as intended.\\n \\n2. **Improper Metric Calculation**: There may be a logical error in the way metrics are calculated or aggregated, such as not accounting for rates correctly.\\n\\n3. **Configuration Issues**: There could be an issue with how Apache is configured, potentially affecting what metrics are exposed and collected by the monitoring tool.\\n\\n**Ranked by likelihood**: \\n1. Incorrect Metric Mapping \\n2. Improper Metric Calculation \\n3. Configuration Issues\\n\\n## SYSTEMATIC INVESTIGATION\\n\\n1. **Review Code for Metric Mapping**:\\n - Examine the sections of code where metrics are defined and mapped to ensure that `apache.net.bytes_per_s` and `apache.net.request_per_s` are correctly included in the collection logic.\\n - Look for any recent changes in the code that might have affected the mapping.\\n\\n2. **Check Metric Calculation Logic**:\\n - Investigate how the metrics are being calculated. Look for functions or methods that aggregate or compute the values for these metrics.\\n - Confirm if the metrics are being treated as rates (per second) and check the logic for calculating rates.\\n\\n3. **Analyze Apache Configuration**:\\n - Validate the Apache configuration to ensure that the necessary modules (like `mod_status`) are enabled and that the URLs used for metric collection are correct.\\n - Check if the Apache server is correctly exposing the expected data needed for the metrics.\\n\\n## ROOT CAUSE IDENTIFICATION\\n\\nUpon reviewing the code changes in the provided patch, it becomes evident that the root cause was related to the way metrics were structured and collected. The initial code had the metrics defined in one section but did not correctly leverage them in the main collection logic. The patch introduced a dedicated section for rates (`RATES`), clearly defining how to collect `apache.net.bytes_per_s` and `apache.net.request_per_s` as rates. \\n\\n## SOLUTION DESIGN\\n\\nThe solution was designed to:\\n- Explicitly separate gauge metrics from rate metrics, improving clarity and correctness in how metrics are collected and reported.\\n- Ensure that the correct metrics are calculated and reported in the way that aligns with the expected definitions used by the monitoring system.\\n\\nThis approach was preferred over simply fixing the existing code because it allowed for better organization and future scalability. The separation of metric types makes it easier to manage and extend.\\n\\n## IMPLEMENTATION REASONING\\n\\n1. **Version Update**:\\n ```python\\n __version__ = \"1.1.2\"\\n ```\\n - Incrementing the version indicates a significant change in functionality that should be noted by users.\\n\\n2. **Defining RATES**:\\n ```python\\n RATES = {\\n \\'Total kBytes\\': \\'apache.net.bytes_per_s\\',\\n \\'Total Accesses\\': \\'apache.net.request_per_s\\'\\n }\\n ```\\n - Clearly defining the metrics that should be treated as rates ensures that the correct calculations are applied.\\n\\n3. **Updating the Metric Collection Logic**:\\n ```python\\n if metric in self.RATES:\\n metric_count += 1\\n metric_name = self.RATES[metric]\\n self.rate(metric_name, value, tags=tags)\\n ```\\n - This change ensures that when a metric is recognized as a rate, it is processed correctly using the `self.rate()` method, allowing for proper rate calculation.\\n\\n## VERIFICATION STRATEGY\\n\\nTo verify the fix:\\n- Test the updated metrics in a local or staging environment to ensure they are correctly reported as expected.\\n- Compare the output against expected values based on known server load and traffic patterns.\\n- Conduct load tests to ensure metrics react correctly under different conditions.\\n- Check edge cases, such as very low traffic scenarios, to ensure metrics do not report erroneous values like zero rates.\\n\\n## PREVENTION & LEARNING\\n\\nTo prevent similar issues in the future:\\n- Implement unit tests for metric collection to catch incorrect mappings or calculations before deployment.\\n- Adopt a metric design pattern that clearly separates different types of metrics (e.g., gauges vs. rates) and ensure that new metrics adhere to this structure.\\n- Regularly review and refactor code related to metrics to maintain clarity and correctness as the application evolves. \\n\\nThis case highlights the importance of clear metric definitions and the need for structured code when dealing with performance monitoring, which can significantly affect user trust and system reliability.',\n",
|
||
" 'role': 'assistant'}]"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dataset[5][\"conversation\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "GfzTdMtvGE6w"
|
||
},
|
||
"source": [
|
||
"And we see how the chat template transformed these conversations."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 175
|
||
},
|
||
"id": "vhXv0xFMGNKE",
|
||
"outputId": "00a2f70d-acb8-4a41-94d8-6a135dc4e059"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "string"
|
||
},
|
||
"text/plain": [
|
||
"'<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n<|im_start|>user\\nHow do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|im_end|>\\n<|im_start|>assistant\\nAstronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|im_end|>\\n'"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dataset[5][\"text\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "idAEIeSQ3xdS"
|
||
},
|
||
"source": [
|
||
"<a name=\"Train\"></a>\n",
|
||
"### Train the model\n",
|
||
"Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!\n",
|
||
"\n",
|
||
"The trainer includes our **gradient accumulation bug fix**. Read more about it here: [Blog post](https://unsloth.ai/blog/gradient)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 67,
|
||
"referenced_widgets": [
|
||
"93ccb0dfad4e4d60a7e7f42402bff80d",
|
||
"8c6346ded74b4614871353d09b685ab3",
|
||
"7a285248d8fa4378860cd5e899613057",
|
||
"8b1c036ade754137a675d55d6ad12e2c",
|
||
"b9304f02c4774b6eb161da6fa6b009ff",
|
||
"7cbc2991535b440b97e02a9df10b306a",
|
||
"ac9c54ff279a4080b7485d669989db6d",
|
||
"ba5b4ef26bc14dadbd444c7a039a2995",
|
||
"18a1a798b3824b33b73f345520583d91",
|
||
"6cbf4a502296486d9b0d8588dc123732",
|
||
"7382f0c83e7b44d3a19bbfda0d3723ee"
|
||
]
|
||
},
|
||
"id": "95_Nn-89DhsL",
|
||
"outputId": "316cba0c-e677-47d5-c678-5b8398facc1e"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "98378c70038042109fe9ee4dad6576b2",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Unsloth: Tokenizing [\"text\"] (num_proc=132): 0%| | 0/10000 [00:00<?, ? examples/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from trl import SFTConfig, SFTTrainer\n",
|
||
"from transformers import DataCollatorForSeq2Seq\n",
|
||
"trainer = SFTTrainer(\n",
|
||
" model = model,\n",
|
||
" tokenizer = tokenizer,\n",
|
||
" train_dataset = dataset,\n",
|
||
" dataset_text_field = \"text\",\n",
|
||
" max_seq_length = max_seq_length,\n",
|
||
" data_collator = DataCollatorForSeq2Seq(tokenizer = tokenizer),\n",
|
||
" packing = False, # Can make training 5x faster for short sequences.\n",
|
||
" args = SFTConfig(\n",
|
||
" per_device_train_batch_size = 1,\n",
|
||
" gradient_accumulation_steps = 4, # Fixed major bug in latest Unsloth\n",
|
||
" warmup_steps = 5,\n",
|
||
" # num_train_epochs = 1, # Set this for 1 full training run.\n",
|
||
" max_steps = 30,\n",
|
||
" learning_rate = 2e-4,\n",
|
||
" logging_steps = 1,\n",
|
||
" optim = \"paged_adamw_8bit\", # Save more memory\n",
|
||
" weight_decay = 0.01,\n",
|
||
" lr_scheduler_type = \"linear\",\n",
|
||
" seed = 3407,\n",
|
||
" output_dir = \"outputs\",\n",
|
||
" report_to = \"none\", # Use this for WandB etc\n",
|
||
" ),\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "C_sGp5XlG6dq"
|
||
},
|
||
"source": [
|
||
"We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 49,
|
||
"referenced_widgets": [
|
||
"4f5037bad4c849f19ccfd7b0f57f6721",
|
||
"d69b25177b214cfb877907376763b4e6",
|
||
"de85541764644bd685f7a950115c2544",
|
||
"5808b66051e943c7ae58cf2dfebf2b41",
|
||
"5ee0f800a0934bc596a2b8249709c6e9",
|
||
"052dc013a23944ca8d1de2d0c7ab8141",
|
||
"52a3d29efbd04ec7858876606b31465c",
|
||
"d94c60b5a75249a894c761c7baf12ff4",
|
||
"e37375168ae64bc1a8c8c1451dc2c4b2",
|
||
"6f98b1e08f954e82adc4565a0b7556c1",
|
||
"a263b7c2a34e41dabc94d78b99835ad8"
|
||
]
|
||
},
|
||
"id": "juQiExuBG5Bt",
|
||
"outputId": "b178bf02-af8d-4550-9e53-778256a2c7fe"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "7a5538a868bc4bc69b0c030c559b8282",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Map (num_proc=128): 0%| | 0/10000 [00:00<?, ? examples/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from unsloth.chat_templates import train_on_responses_only\n",
|
||
"trainer = train_on_responses_only(\n",
|
||
" trainer,\n",
|
||
" instruction_part = \"<|im_start|>user\\n\",\n",
|
||
" response_part = \"<|im_start|>assistant\\n\",\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "Dv1NBUozV78l"
|
||
},
|
||
"source": [
|
||
"We verify masking is actually done:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 175
|
||
},
|
||
"id": "LtsMVtlkUhja",
|
||
"outputId": "cadeca7a-43d9-4a4b-bc80-c25cff082e2e"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'<|im_start|>system\\nYou are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.<|im_end|>\\n<|im_start|>user\\nI\\'m having an issue with the DataDog/integrations-core codebase. Here\\'s the problem:\\n\\n[apache] fix metric collection\\n### What does this PR do?\\r\\n\\r\\nThis collects the correct data for `apache.net.bytes_per_s` and `apache.net.request_per_s`.\\r\\n\\r\\nhttps://www.datadoghq.com/blog/collect-apache-performance-metrics\\r\\nhttps://wiki.opennms.org/wiki/Monitoring_Apache_with_the_HTTP_collector\\r\\nhttps://httpd.apache.org/docs/current/mod/mod_status.html\\r\\n\\r\\n### Motivation\\r\\n\\r\\nCustomer encountered bug\\n\\n\\nCan you help me debug this systematically and explain your reasoning?<|im_end|>\\n<|im_start|>assistant\\n## INITIAL PROBLEM ASSESSMENT\\n\\nThe bug report indicates that there was an issue with metric collection for Apache performance metrics, specifically `apache.net.bytes_per_s` and `apache.net.request_per_s`. This suggests that the application was not reporting accurate or expected values for these metrics, which could lead to misinterpretation of server performance by users relying on this data.\\n\\nSymptoms might include:\\n- Users not seeing expected values in monitoring dashboards.\\n- Discrepancies in data reported over time, potentially leading to false conclusions about server load and performance.\\n- Customer complaints about the monitoring system\\'s reliability.\\n\\n## HYPOTHESIS FORMATION\\n\\n1. **Incorrect Metric Mapping**: The metrics `apache.net.bytes_per_s` and `apache.net.request_per_s` might not be correctly mapped in the code, leading to values not being collected or reported as intended.\\n \\n2. **Improper Metric Calculation**: There may be a logical error in the way metrics are calculated or aggregated, such as not accounting for rates correctly.\\n\\n3. **Configuration Issues**: There could be an issue with how Apache is configured, potentially affecting what metrics are exposed and collected by the monitoring tool.\\n\\n**Ranked by likelihood**: \\n1. Incorrect Metric Mapping \\n2. Improper Metric Calculation \\n3. Configuration Issues\\n\\n## SYSTEMATIC INVESTIGATION\\n\\n1. **Review Code for Metric Mapping**:\\n - Examine the sections of code where metrics are defined and mapped to ensure that `apache.net.bytes_per_s` and `apache.net.request_per_s` are correctly included in the collection logic.\\n - Look for any recent changes in the code that might have affected the mapping.\\n\\n2. **Check Metric Calculation Logic**:\\n - Investigate how the metrics are being calculated. Look for functions or methods that aggregate or compute the values for these metrics.\\n - Confirm if the metrics are being treated as rates (per second) and check the logic for calculating rates.\\n\\n3. **Analyze Apache Configuration**:\\n - Validate the Apache configuration to ensure that the necessary modules (like `mod_status`) are enabled and that the URLs used for metric collection are correct.\\n - Check if the Apache server is correctly exposing the expected data needed for the metrics.\\n\\n## ROOT CAUSE IDENTIFICATION\\n\\nUpon reviewing the code changes in the provided patch, it becomes evident that the root cause was related to the way metrics were structured and collected. The initial code had the metrics defined in one section but did not correctly leverage them in the main collection logic. The patch introduced a dedicated section for rates (`RATES`), clearly defining how to collect `apache.net.bytes_per_s` and `apache.net.request_per_s` as rates. \\n\\n## SOLUTION DESIGN\\n\\nThe solution was designed to:\\n- Explicitly separate gauge metrics from rate metrics, improving clarity and correctness in how metrics are collected and reported.\\n- Ensure that the correct metrics are calculated and reported in the way that aligns with the expected definitions used by the monitoring system.\\n\\nThis approach was preferred over simply fixing the existing code because it allowed for better organization and future scalability. The separation of metric types makes it easier to manage and extend.\\n\\n## IMPLEMENTATION REASONING\\n\\n1. **Version Update**:\\n ```python\\n __version__ = \"1.1.2\"\\n ```\\n - Incrementing the version indicates a significant change in functionality that should be noted by users.\\n\\n2. **Defining RATES**:\\n ```python\\n RATES = {\\n \\'Total kBytes\\': \\'apache.net.bytes_per_s\\',\\n \\'Total Accesses\\': \\'apache.net.request_per_s\\'\\n }\\n ```\\n - Clearly defining the metrics that should be treated as rates ensures that the correct calculations are applied.\\n\\n3. **Updating the Metric Collection Logic**:\\n ```python\\n if metric in self.RATES:\\n metric_count += 1\\n metric_name = self.RATES[metric]\\n self.rate(metric_name, value, tags=tags)\\n ```\\n - This change ensures that when a metric is recognized as a rate, it is processed correctly using the `self.rate()` method, allowing for proper rate calculation.\\n\\n## VERIFICATION STRATEGY\\n\\nTo verify the fix:\\n- Test the updated metrics in a local or staging environment to ensure they are correctly reported as expected.\\n- Compare the output against expected values based on known server load and traffic patterns.\\n- Conduct load tests to ensure metrics react correctly under different conditions.\\n- Check edge cases, such as very low traffic scenarios, to ensure metrics do not report erroneous values like zero rates.\\n\\n## PREVENTION & LEARNING\\n\\nTo prevent similar issues in the future:\\n- Implement unit tests for metric collection to catch incorrect mappings or calculations before deployment.\\n- Adopt a metric design pattern that clearly separates different types of metrics (e.g., gauges vs. rates) and ensure that new metrics adhere to this structure.\\n- Regularly review and refactor code related to metrics to maintain clarity and correctness as the application evolves. \\n\\nThis case highlights the importance of clear metric definitions and the need for structured code when dealing with performance monitoring, which can significantly affect user trust and system reliability.<|im_end|>\\n'"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"tokenizer.decode(trainer.train_dataset[5][\"input_ids\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 140
|
||
},
|
||
"id": "_rD6fl8EUxnG",
|
||
"outputId": "b1da5770-3de6-48d5-c4dc-890075d0b6b0"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"' ## INITIAL PROBLEM ASSESSMENT\\n\\nThe bug report indicates that there was an issue with metric collection for Apache performance metrics, specifically `apache.net.bytes_per_s` and `apache.net.request_per_s`. This suggests that the application was not reporting accurate or expected values for these metrics, which could lead to misinterpretation of server performance by users relying on this data.\\n\\nSymptoms might include:\\n- Users not seeing expected values in monitoring dashboards.\\n- Discrepancies in data reported over time, potentially leading to false conclusions about server load and performance.\\n- Customer complaints about the monitoring system\\'s reliability.\\n\\n## HYPOTHESIS FORMATION\\n\\n1. **Incorrect Metric Mapping**: The metrics `apache.net.bytes_per_s` and `apache.net.request_per_s` might not be correctly mapped in the code, leading to values not being collected or reported as intended.\\n \\n2. **Improper Metric Calculation**: There may be a logical error in the way metrics are calculated or aggregated, such as not accounting for rates correctly.\\n\\n3. **Configuration Issues**: There could be an issue with how Apache is configured, potentially affecting what metrics are exposed and collected by the monitoring tool.\\n\\n**Ranked by likelihood**: \\n1. Incorrect Metric Mapping \\n2. Improper Metric Calculation \\n3. Configuration Issues\\n\\n## SYSTEMATIC INVESTIGATION\\n\\n1. **Review Code for Metric Mapping**:\\n - Examine the sections of code where metrics are defined and mapped to ensure that `apache.net.bytes_per_s` and `apache.net.request_per_s` are correctly included in the collection logic.\\n - Look for any recent changes in the code that might have affected the mapping.\\n\\n2. **Check Metric Calculation Logic**:\\n - Investigate how the metrics are being calculated. Look for functions or methods that aggregate or compute the values for these metrics.\\n - Confirm if the metrics are being treated as rates (per second) and check the logic for calculating rates.\\n\\n3. **Analyze Apache Configuration**:\\n - Validate the Apache configuration to ensure that the necessary modules (like `mod_status`) are enabled and that the URLs used for metric collection are correct.\\n - Check if the Apache server is correctly exposing the expected data needed for the metrics.\\n\\n## ROOT CAUSE IDENTIFICATION\\n\\nUpon reviewing the code changes in the provided patch, it becomes evident that the root cause was related to the way metrics were structured and collected. The initial code had the metrics defined in one section but did not correctly leverage them in the main collection logic. The patch introduced a dedicated section for rates (`RATES`), clearly defining how to collect `apache.net.bytes_per_s` and `apache.net.request_per_s` as rates. \\n\\n## SOLUTION DESIGN\\n\\nThe solution was designed to:\\n- Explicitly separate gauge metrics from rate metrics, improving clarity and correctness in how metrics are collected and reported.\\n- Ensure that the correct metrics are calculated and reported in the way that aligns with the expected definitions used by the monitoring system.\\n\\nThis approach was preferred over simply fixing the existing code because it allowed for better organization and future scalability. The separation of metric types makes it easier to manage and extend.\\n\\n## IMPLEMENTATION REASONING\\n\\n1. **Version Update**:\\n ```python\\n __version__ = \"1.1.2\"\\n ```\\n - Incrementing the version indicates a significant change in functionality that should be noted by users.\\n\\n2. **Defining RATES**:\\n ```python\\n RATES = {\\n \\'Total kBytes\\': \\'apache.net.bytes_per_s\\',\\n \\'Total Accesses\\': \\'apache.net.request_per_s\\'\\n }\\n ```\\n - Clearly defining the metrics that should be treated as rates ensures that the correct calculations are applied.\\n\\n3. **Updating the Metric Collection Logic**:\\n ```python\\n if metric in self.RATES:\\n metric_count += 1\\n metric_name = self.RATES[metric]\\n self.rate(metric_name, value, tags=tags)\\n ```\\n - This change ensures that when a metric is recognized as a rate, it is processed correctly using the `self.rate()` method, allowing for proper rate calculation.\\n\\n## VERIFICATION STRATEGY\\n\\nTo verify the fix:\\n- Test the updated metrics in a local or staging environment to ensure they are correctly reported as expected.\\n- Compare the output against expected values based on known server load and traffic patterns.\\n- Conduct load tests to ensure metrics react correctly under different conditions.\\n- Check edge cases, such as very low traffic scenarios, to ensure metrics do not report erroneous values like zero rates.\\n\\n## PREVENTION & LEARNING\\n\\nTo prevent similar issues in the future:\\n- Implement unit tests for metric collection to catch incorrect mappings or calculations before deployment.\\n- Adopt a metric design pattern that clearly separates different types of metrics (e.g., gauges vs. rates) and ensure that new metrics adhere to this structure.\\n- Regularly review and refactor code related to metrics to maintain clarity and correctness as the application evolves. \\n\\nThis case highlights the importance of clear metric definitions and the need for structured code when dealing with performance monitoring, which can significantly affect user trust and system reliability.<|im_end|>\\n'"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"space = tokenizer(\" \", add_special_tokens = False).input_ids[0]\n",
|
||
"tokenizer.decode([space if x == -100 else x for x in trainer.train_dataset[5][\"labels\"]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "3enWUM0jV-jV"
|
||
},
|
||
"source": [
|
||
"We can see the System and Instruction prompts are successfully masked!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"cellView": "form",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "2ejIt2xSNKKp",
|
||
"outputId": "faf37c55-a0ad-41a3-aeed-55ee7d7070e0"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"GPU = NVIDIA A100-SXM4-80GB. Max memory = 79.254 GB.\n",
|
||
"39.311 GB of memory reserved.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# @title Show current memory stats\n",
|
||
"gpu_stats = torch.cuda.get_device_properties(0)\n",
|
||
"start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
|
||
"max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
|
||
"print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
|
||
"print(f\"{start_gpu_memory} GB of memory reserved.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "r3WJRxG-zoeS"
|
||
},
|
||
"source": [
|
||
"We fixed a major gradient accumulation bug in all trainers. See [blog](https://unsloth.ai/blog/gradient) for more details."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "yqxqAZ7KJ4oL",
|
||
"outputId": "f401195d-1ff9-4a9f-8a8e-ccda6bf3c0ec"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
|
||
" \\\\ /| Num examples = 10,000 | Num Epochs = 1 | Total steps = 30\n",
|
||
"O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 4\n",
|
||
"\\ / Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n",
|
||
" \"-____-\" Trainable parameters = 207,093,760 of 70,760,800,256 (0.29% trained)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Unsloth: Will smartly offload gradients to save VRAM!\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"\n",
|
||
" <div>\n",
|
||
" \n",
|
||
" <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||
" [30/30 08:55, Epoch 0/1]\n",
|
||
" </div>\n",
|
||
" <table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: left;\">\n",
|
||
" <th>Step</th>\n",
|
||
" <th>Training Loss</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.392200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1.410800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1.352200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>4</td>\n",
|
||
" <td>1.363400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1.398400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>6</td>\n",
|
||
" <td>1.298400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>7</td>\n",
|
||
" <td>1.222500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>8</td>\n",
|
||
" <td>1.159400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>9</td>\n",
|
||
" <td>1.099800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>10</td>\n",
|
||
" <td>1.055300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>11</td>\n",
|
||
" <td>1.043900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>12</td>\n",
|
||
" <td>0.978400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>13</td>\n",
|
||
" <td>1.063100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>14</td>\n",
|
||
" <td>1.062000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>15</td>\n",
|
||
" <td>1.058900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>16</td>\n",
|
||
" <td>1.033100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>17</td>\n",
|
||
" <td>1.024000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>18</td>\n",
|
||
" <td>1.005400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>19</td>\n",
|
||
" <td>1.060700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>20</td>\n",
|
||
" <td>1.103800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>21</td>\n",
|
||
" <td>0.956300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>22</td>\n",
|
||
" <td>0.981500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>23</td>\n",
|
||
" <td>0.957100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>24</td>\n",
|
||
" <td>1.063800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>25</td>\n",
|
||
" <td>1.021500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>26</td>\n",
|
||
" <td>0.982600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>27</td>\n",
|
||
" <td>0.953900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>28</td>\n",
|
||
" <td>0.884000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>29</td>\n",
|
||
" <td>0.981600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>30</td>\n",
|
||
" <td>0.946500</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table><p>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"trainer_stats = trainer.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"cellView": "form",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "pCqnaKmlO1U9",
|
||
"outputId": "23a21d88-0604-495f-eb05-92c34ce64765"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"608.5086 seconds used for training.\n",
|
||
"10.14 minutes used for training.\n",
|
||
"Peak reserved memory = 45.48 GB.\n",
|
||
"Peak reserved memory for training = 6.169 GB.\n",
|
||
"Peak reserved memory % of max memory = 57.385 %.\n",
|
||
"Peak reserved memory for training % of max memory = 7.784 %.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# @title Show final memory and time stats\n",
|
||
"used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
|
||
"used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
|
||
"used_percentage = round(used_memory / max_memory * 100, 3)\n",
|
||
"lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
|
||
"print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
|
||
"print(\n",
|
||
" f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
|
||
")\n",
|
||
"print(f\"Peak reserved memory = {used_memory} GB.\")\n",
|
||
"print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
|
||
"print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
|
||
"print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ekOmTR1hSNcr"
|
||
},
|
||
"source": [
|
||
"<a name=\"Inference\"></a>\n",
|
||
"### Inference\n",
|
||
"Let's run the model! You can change the instruction and input - leave the output blank!\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"We use `min_p = 0.1` and `temperature = 1.5`. Read this [Tweet](https://x.com/menhguin/status/1826132708508213629) for more information on why."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'conversation': [{'content': 'You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.',\n",
|
||
" 'role': 'system'},\n",
|
||
" {'content': 'I\\'m having an issue with the DataDog/integrations-core codebase. Here\\'s the problem:\\n\\nElasticsearch check fails when used with Opensearch\\n**Note:** If you have a feature request, you should [contact support](https://docs.datadoghq.com/help/) so the request can be properly tracked.\\r\\n\\r\\n**Output of the [info page](https://docs.datadoghq.com/agent/guide/agent-commands/#agent-status-and-information)**\\r\\n\\r\\n```text\\r\\n elastic (1.24.0)\\r\\n ----------------\\r\\n Instance ID: elastic:e6126c6cf9b8eebe [ERROR]\\r\\n Configuration Source: file:/etc/datadog-agent/conf.d/elastic.yaml\\r\\n Total Runs: 121\\r\\n Metric Samples: Last Run: 0, Total: 0\\r\\n Events: Last Run: 0, Total: 0\\r\\n Service Checks: Last Run: 1, Total: 121\\r\\n Average Execution Time : 13ms\\r\\n Last Execution Date : 2021-09-10 06:00:16 UTC (1631253616000)\\r\\n Last Successful Execution Date : Never\\r\\n metadata:\\r\\n version.major: 1\\r\\n version.minor: 0\\r\\n version.patch: 0\\r\\n version.raw: 1.0.0\\r\\n version.scheme: semver\\r\\n Error: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n Traceback (most recent call last):\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\", line 897, in run\\r\\n self.check(instance)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 79, in check\\r\\n stats_data = self._get_data(stats_url)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 232, in _get_data\\r\\n resp.raise_for_status()\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\", line 940, in raise_for_status\\r\\n raise HTTPError(http_error_msg, response=self)\\r\\n requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n\\r\\n```\\r\\n\\r\\n**Additional environment details (Operating System, Cloud provider, etc):**\\r\\nFedora 33 running agent inside systemd-nspawn container. Container is running Opensearch 1.0.0 and datadog agent has been configured to monitor it.\\r\\n\\r\\n**Steps to reproduce the issue:**\\r\\n1. Install and run Opensearch\\r\\n2. Configure datadog agent Elasticsearch integration to monitor it\\r\\n3. Observe that the Elasticsearch check is failing\\r\\n\\r\\n**Describe the results you received:**\\r\\n\\r\\nThe Elasticsearch check fails in the following way:\\r\\n```text\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:261 in work) | check:elastic | Running check\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/check.go:81 in runCheck) | Running python check elastic elastic:878a21a7a9cbcd49\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET / HTTP/1.1\" 200 349\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:247) | request to url http://localhost:9200 returned: <Response [200]>\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:147) | Elasticsearch version is [1, 0, 0]\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET /_nodes/_local/stats?all=true HTTP/1.1\" 400 155\\r\\n2021-09-09 12:48:49 UTC | CORE | ERROR | (pkg/collector/runner/runner.go:292 in work) | Error running check elastic: [{\"message\": \"400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\", \"traceback\": \"Traceback (most recent call last):\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\\\\\", line 897, in run\\\\n self.check(instance)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 79, in check\\\\n stats_data = self._get_data(stats_url)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 232, in _get_data\\\\n resp.raise_for_status()\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\\\\\", line 940, in raise_for_status\\\\n raise HTTPError(http_error_msg, response=self)\\\\nrequests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\\\n\"}]\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:327 in work) | check:elastic | Done running check\\r\\n```\\r\\n\\r\\n**Describe the results you expected:**\\r\\n\\r\\nOpensearch 1.0.0 is fully compatible with Elasticsearch 7.10.2 and datadog agent should be able to monitor it in the same way as the Elasticsearch.\\r\\n\\r\\n**Additional information you deem important (e.g. issue happens only occasionally):**\\r\\n\\r\\nThe issue seems to be that since Opensearch is reporting its version as `1.0.0` the Elasticsearch check interprets it as Elasticsearch `1.0.0` and for this reason adds `?all=true` to the URL when calling `/_nodes/_local/stats` API to collect the node stats. https://github.com/DataDog/integrations-core/blob/40d9ceb14c57c109e8f6371b1a4c677fa33e1669/elastic/datadog_checks/elastic/elastic.py#L215\\r\\n\\r\\nThe `?all=true` parameter has been depricated since Elasticsearch version `5.0.0` and results in HTTP 400 error when used with newer versions of Elasticsearch. All is working well when Elasticsearch check is monitoring Opensearch if `?all=true` has been left out when making requests to `/_nodes/_local/stats`.\\r\\n\\r\\nThe version information returned from Opensearch contains field called `distribution` which has value `opensearch` that could be used to detect that the check is actually running against an Opensearch server. Example here:\\r\\n```text\\r\\n{\\'name\\': \\'os-355e47a7-1\\', \\'cluster_name\\': \\'fc7a706a-9c51-40a1-9e1b-953d1546caa0\\', \\'cluster_uuid\\': \\'HrCNW5uhSZKtHxlJQz444g\\', \\'version\\': {\\'distribution\\': \\'opensearch\\', \\'number\\': \\'1.0.0\\', \\'build_type\\': \\'unknown\\', \\'build_hash\\': \\'unknown\\', \\'build_date\\': \\'2021-08-13T12:38:05.881264Z\\', \\'build_snapshot\\': False, \\'lucene_version\\': \\'8.8.2\\', \\'minimum_wire_compatibility_version\\': \\'6.8.0\\', \\'minimum_index_compatibility_version\\': \\'6.0.0-beta1\\'}, \\'tagline\\': \\'The OpenSearch Project: https://opensearch.org/\\'}\\r\\n```\\n\\n\\nCan you help me debug this systematically and explain your reasoning?',\n",
|
||
" 'role': 'user'},\n",
|
||
" {'content': '', 'role': 'assistant'}],\n",
|
||
" 'metadata': {'base_commit': '160cfef6e1118061fa66d333e8c2a572f5d0a815',\n",
|
||
" 'dataset': 'princeton-nlp/SWE-bench',\n",
|
||
" 'generation_timestamp': '2025-08-19T23:35:38.773997',\n",
|
||
" 'has_patch': True,\n",
|
||
" 'instance_id': 'DataDog__integrations-core-10093',\n",
|
||
" 'original_index': 0,\n",
|
||
" 'reasoning_length': 0,\n",
|
||
" 'repo': 'DataDog/integrations-core',\n",
|
||
" 'source': 'swe-bench'},\n",
|
||
" 'original_example': {'instance_id': 'DataDog__integrations-core-10093',\n",
|
||
" 'patch': \"diff --git a/elastic/datadog_checks/elastic/elastic.py b/elastic/datadog_checks/elastic/elastic.py\\n--- a/elastic/datadog_checks/elastic/elastic.py\\n+++ b/elastic/datadog_checks/elastic/elastic.py\\n@@ -139,11 +139,17 @@ def _get_es_version(self):\\n try:\\n data = self._get_data(self._config.url, send_sc=False)\\n raw_version = data['version']['number']\\n+\\n self.set_metadata('version', raw_version)\\n # pre-release versions of elasticearch are suffixed with -rcX etc..\\n # peel that off so that the map below doesn't error out\\n raw_version = raw_version.split('-')[0]\\n version = [int(p) for p in raw_version.split('.')[0:3]]\\n+ if data['version'].get('distribution', '') == 'opensearch':\\n+ # Opensearch API is backwards compatible with ES 7.10.0\\n+ # https://opensearch.org/faq\\n+ self.log.debug('OpenSearch version %s detected', version)\\n+ version = [7, 10, 0]\\n except AuthenticationError:\\n raise\\n except Exception as e:\\n\",\n",
|
||
" 'problem_statement': 'Elasticsearch check fails when used with Opensearch\\n**Note:** If you have a feature request, you should [contact support](https://docs.datadoghq.com/help/) so the request can be properly tracked.\\r\\n\\r\\n**Output of the [info page](https://docs.datadoghq.com/agent/guide/agent-commands/#agent-status-and-information)**\\r\\n\\r\\n```text\\r\\n elastic (1.24.0)\\r\\n ----------------\\r\\n Instance ID: elastic:e6126c6cf9b8eebe [ERROR]\\r\\n Configuration Source: file:/etc/datadog-agent/conf.d/elastic.yaml\\r\\n Total Runs: 121\\r\\n Metric Samples: Last Run: 0, Total: 0\\r\\n Events: Last Run: 0, Total: 0\\r\\n Service Checks: Last Run: 1, Total: 121\\r\\n Average Execution Time : 13ms\\r\\n Last Execution Date : 2021-09-10 06:00:16 UTC (1631253616000)\\r\\n Last Successful Execution Date : Never\\r\\n metadata:\\r\\n version.major: 1\\r\\n version.minor: 0\\r\\n version.patch: 0\\r\\n version.raw: 1.0.0\\r\\n version.scheme: semver\\r\\n Error: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n Traceback (most recent call last):\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\", line 897, in run\\r\\n self.check(instance)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 79, in check\\r\\n stats_data = self._get_data(stats_url)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 232, in _get_data\\r\\n resp.raise_for_status()\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\", line 940, in raise_for_status\\r\\n raise HTTPError(http_error_msg, response=self)\\r\\n requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n\\r\\n```\\r\\n\\r\\n**Additional environment details (Operating System, Cloud provider, etc):**\\r\\nFedora 33 running agent inside systemd-nspawn container. Container is running Opensearch 1.0.0 and datadog agent has been configured to monitor it.\\r\\n\\r\\n**Steps to reproduce the issue:**\\r\\n1. Install and run Opensearch\\r\\n2. Configure datadog agent Elasticsearch integration to monitor it\\r\\n3. Observe that the Elasticsearch check is failing\\r\\n\\r\\n**Describe the results you received:**\\r\\n\\r\\nThe Elasticsearch check fails in the following way:\\r\\n```text\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:261 in work) | check:elastic | Running check\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/check.go:81 in runCheck) | Running python check elastic elastic:878a21a7a9cbcd49\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET / HTTP/1.1\" 200 349\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:247) | request to url http://localhost:9200 returned: <Response [200]>\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:147) | Elasticsearch version is [1, 0, 0]\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET /_nodes/_local/stats?all=true HTTP/1.1\" 400 155\\r\\n2021-09-09 12:48:49 UTC | CORE | ERROR | (pkg/collector/runner/runner.go:292 in work) | Error running check elastic: [{\"message\": \"400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\", \"traceback\": \"Traceback (most recent call last):\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\\\\\", line 897, in run\\\\n self.check(instance)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 79, in check\\\\n stats_data = self._get_data(stats_url)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 232, in _get_data\\\\n resp.raise_for_status()\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\\\\\", line 940, in raise_for_status\\\\n raise HTTPError(http_error_msg, response=self)\\\\nrequests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\\\n\"}]\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:327 in work) | check:elastic | Done running check\\r\\n```\\r\\n\\r\\n**Describe the results you expected:**\\r\\n\\r\\nOpensearch 1.0.0 is fully compatible with Elasticsearch 7.10.2 and datadog agent should be able to monitor it in the same way as the Elasticsearch.\\r\\n\\r\\n**Additional information you deem important (e.g. issue happens only occasionally):**\\r\\n\\r\\nThe issue seems to be that since Opensearch is reporting its version as `1.0.0` the Elasticsearch check interprets it as Elasticsearch `1.0.0` and for this reason adds `?all=true` to the URL when calling `/_nodes/_local/stats` API to collect the node stats. https://github.com/DataDog/integrations-core/blob/40d9ceb14c57c109e8f6371b1a4c677fa33e1669/elastic/datadog_checks/elastic/elastic.py#L215\\r\\n\\r\\nThe `?all=true` parameter has been depricated since Elasticsearch version `5.0.0` and results in HTTP 400 error when used with newer versions of Elasticsearch. All is working well when Elasticsearch check is monitoring Opensearch if `?all=true` has been left out when making requests to `/_nodes/_local/stats`.\\r\\n\\r\\nThe version information returned from Opensearch contains field called `distribution` which has value `opensearch` that could be used to detect that the check is actually running against an Opensearch server. Example here:\\r\\n```text\\r\\n{\\'name\\': \\'os-355e47a7-1\\', \\'cluster_name\\': \\'fc7a706a-9c51-40a1-9e1b-953d1546caa0\\', \\'cluster_uuid\\': \\'HrCNW5uhSZKtHxlJQz444g\\', \\'version\\': {\\'distribution\\': \\'opensearch\\', \\'number\\': \\'1.0.0\\', \\'build_type\\': \\'unknown\\', \\'build_hash\\': \\'unknown\\', \\'build_date\\': \\'2021-08-13T12:38:05.881264Z\\', \\'build_snapshot\\': False, \\'lucene_version\\': \\'8.8.2\\', \\'minimum_wire_compatibility_version\\': \\'6.8.0\\', \\'minimum_index_compatibility_version\\': \\'6.0.0-beta1\\'}, \\'tagline\\': \\'The OpenSearch Project: https://opensearch.org/\\'}\\r\\n```\\n',\n",
|
||
" 'repo': 'DataDog/integrations-core'},\n",
|
||
" 'text': '<|im_start|>system\\nYou are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.<|im_end|>\\n<|im_start|>user\\nI\\'m having an issue with the DataDog/integrations-core codebase. Here\\'s the problem:\\n\\nElasticsearch check fails when used with Opensearch\\n**Note:** If you have a feature request, you should [contact support](https://docs.datadoghq.com/help/) so the request can be properly tracked.\\r\\n\\r\\n**Output of the [info page](https://docs.datadoghq.com/agent/guide/agent-commands/#agent-status-and-information)**\\r\\n\\r\\n```text\\r\\n elastic (1.24.0)\\r\\n ----------------\\r\\n Instance ID: elastic:e6126c6cf9b8eebe [ERROR]\\r\\n Configuration Source: file:/etc/datadog-agent/conf.d/elastic.yaml\\r\\n Total Runs: 121\\r\\n Metric Samples: Last Run: 0, Total: 0\\r\\n Events: Last Run: 0, Total: 0\\r\\n Service Checks: Last Run: 1, Total: 121\\r\\n Average Execution Time : 13ms\\r\\n Last Execution Date : 2021-09-10 06:00:16 UTC (1631253616000)\\r\\n Last Successful Execution Date : Never\\r\\n metadata:\\r\\n version.major: 1\\r\\n version.minor: 0\\r\\n version.patch: 0\\r\\n version.raw: 1.0.0\\r\\n version.scheme: semver\\r\\n Error: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n Traceback (most recent call last):\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\", line 897, in run\\r\\n self.check(instance)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 79, in check\\r\\n stats_data = self._get_data(stats_url)\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\", line 232, in _get_data\\r\\n resp.raise_for_status()\\r\\n File \"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\", line 940, in raise_for_status\\r\\n raise HTTPError(http_error_msg, response=self)\\r\\n requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\r\\n\\r\\n```\\r\\n\\r\\n**Additional environment details (Operating System, Cloud provider, etc):**\\r\\nFedora 33 running agent inside systemd-nspawn container. Container is running Opensearch 1.0.0 and datadog agent has been configured to monitor it.\\r\\n\\r\\n**Steps to reproduce the issue:**\\r\\n1. Install and run Opensearch\\r\\n2. Configure datadog agent Elasticsearch integration to monitor it\\r\\n3. Observe that the Elasticsearch check is failing\\r\\n\\r\\n**Describe the results you received:**\\r\\n\\r\\nThe Elasticsearch check fails in the following way:\\r\\n```text\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:261 in work) | check:elastic | Running check\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/check.go:81 in runCheck) | Running python check elastic elastic:878a21a7a9cbcd49\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET / HTTP/1.1\" 200 349\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:247) | request to url http://localhost:9200 returned: <Response [200]>\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | elastic:878a21a7a9cbcd49 | (elastic.py:147) | Elasticsearch version is [1, 0, 0]\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:226) | Starting new HTTP connection (1): localhost:9200\\r\\n2021-09-09 12:48:49 UTC | CORE | DEBUG | (pkg/collector/python/datadog_agent.go:126 in LogMessage) | - | (connectionpool.py:433) | http://localhost:9200 \"GET /_nodes/_local/stats?all=true HTTP/1.1\" 400 155\\r\\n2021-09-09 12:48:49 UTC | CORE | ERROR | (pkg/collector/runner/runner.go:292 in work) | Error running check elastic: [{\"message\": \"400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\", \"traceback\": \"Traceback (most recent call last):\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/base/checks/base.py\\\\\", line 897, in run\\\\n self.check(instance)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 79, in check\\\\n stats_data = self._get_data(stats_url)\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/datadog_checks/elastic/elastic.py\\\\\", line 232, in _get_data\\\\n resp.raise_for_status()\\\\n File \\\\\"/opt/datadog-agent/embedded/lib/python3.8/site-packages/requests/models.py\\\\\", line 940, in raise_for_status\\\\n raise HTTPError(http_error_msg, response=self)\\\\nrequests.exceptions.HTTPError: 400 Client Error: Bad Request for url: http://localhost:9200/_nodes/_local/stats?all=true\\\\n\"}]\\r\\n2021-09-09 12:48:49 UTC | CORE | INFO | (pkg/collector/runner/runner.go:327 in work) | check:elastic | Done running check\\r\\n```\\r\\n\\r\\n**Describe the results you expected:**\\r\\n\\r\\nOpensearch 1.0.0 is fully compatible with Elasticsearch 7.10.2 and datadog agent should be able to monitor it in the same way as the Elasticsearch.\\r\\n\\r\\n**Additional information you deem important (e.g. issue happens only occasionally):**\\r\\n\\r\\nThe issue seems to be that since Opensearch is reporting its version as `1.0.0` the Elasticsearch check interprets it as Elasticsearch `1.0.0` and for this reason adds `?all=true` to the URL when calling `/_nodes/_local/stats` API to collect the node stats. https://github.com/DataDog/integrations-core/blob/40d9ceb14c57c109e8f6371b1a4c677fa33e1669/elastic/datadog_checks/elastic/elastic.py#L215\\r\\n\\r\\nThe `?all=true` parameter has been depricated since Elasticsearch version `5.0.0` and results in HTTP 400 error when used with newer versions of Elasticsearch. All is working well when Elasticsearch check is monitoring Opensearch if `?all=true` has been left out when making requests to `/_nodes/_local/stats`.\\r\\n\\r\\nThe version information returned from Opensearch contains field called `distribution` which has value `opensearch` that could be used to detect that the check is actually running against an Opensearch server. Example here:\\r\\n```text\\r\\n{\\'name\\': \\'os-355e47a7-1\\', \\'cluster_name\\': \\'fc7a706a-9c51-40a1-9e1b-953d1546caa0\\', \\'cluster_uuid\\': \\'HrCNW5uhSZKtHxlJQz444g\\', \\'version\\': {\\'distribution\\': \\'opensearch\\', \\'number\\': \\'1.0.0\\', \\'build_type\\': \\'unknown\\', \\'build_hash\\': \\'unknown\\', \\'build_date\\': \\'2021-08-13T12:38:05.881264Z\\', \\'build_snapshot\\': False, \\'lucene_version\\': \\'8.8.2\\', \\'minimum_wire_compatibility_version\\': \\'6.8.0\\', \\'minimum_index_compatibility_version\\': \\'6.0.0-beta1\\'}, \\'tagline\\': \\'The OpenSearch Project: https://opensearch.org/\\'}\\r\\n```\\n\\n\\nCan you help me debug this systematically and explain your reasoning?<|im_end|>\\n<|im_start|>assistant\\n<|im_end|>\\n'}"
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dataset[0]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "kR3gIAX-SM2q",
|
||
"outputId": "61a9d3ff-1ecd-4af2-a3c7-a17045af4fd0"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"['<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n<|im_start|>user\\nHelp me resolve the code: see the error I was getting\\n\\n#!/usr/bin/env python3\\n\"\"\"\\nStyling Inference Script\\nProvides a command-line interface to run the styling inference pipeline\\n\"\"\"\\n\\nimport sys\\nimport os\\nimport subprocess\\nimport argparse\\nfrom pathlib import Path\\n\\ndef run_inference_with_config(config_path: str, instruction: str, input_text: str = \"\", max_tokens: int = 128, stream: bool = False):\\n \"\"\"Run inference using a YAML configuration file\"\"\"\\n print(f\"Running styling inference with config: {config_path}\")\\n print(f\"Instruction: {instruction}\")\\n print(f\"Input text: {input_text}\")\\n print(f\"Max tokens: {max_tokens}\")\\n print(f\"Streaming: {stream}\")\\n\\n cmd = [\\n \"python\", \"pipelines/styling/inference.py\",\\n \"--config\", config_path,\\n \"--instruction\", instruction,\\n \"--input-text\", input_text,\\n \"--max-tokens\", str(max_tokens)\\n ]\\n\\n if stream:\\n cmd.append(\"--stream\")\\n\\n print(f\"Running: {\\' \\'.join(cmd)}\")\\n\\n try:\\n result = subprocess.run(cmd, capture_output=True, text=True, check=True)\\n print(\"✅ Inference completed successfully!\")\\n print(\"Output:\")\\n print(result.stdout)\\n return result.stdout\\n except subprocess.CalledProcessError as e:\\n print(f\"❌ Inference failed: {e}\")\\n print(\"Error output:\")\\n print(e.stderr)\\n return None\\n\\ndef run_batch_inference_example(config_path: str, input_file: str, output_file: str, instruction: str, max_tokens: int = 128):\\n \"\"\"Run batch inference example\"\"\"\\n print(f\"=== Batch Inference Example ===\")\\n print(f\"Config: {config_path}\")\\n print(f\"Input file: {input_file}\")\\n print(f\"Output file: {output_file}\")\\n print(f\"Instruction: {instruction}\")\\n print(f\"Max tokens: {max_tokens}\")\\n\\n # Check if input file exists\\n if not Path(input_file).exists():\\n print(f\"❌ Input file not found: {input_file}\")\\n return False\\n\\n # Read input texts\\n with open(input_file, \\'r\\', encoding=\\'utf-8\\') as f:\\n input_texts = [line.strip() for line in f if line.strip()]\\n\\n print(f\"Found {len(input_texts)} texts to process\")\\n\\n # Process each text\\n results = []\\n for i, text in enumerate(input_texts):\\n print(f\"\\\\nProcessing text {i+1}/{len(input_texts)}: {text[:50]}...\")\\n result = run_inference_with_config(config_path, instruction, text, max_tokens)\\n if result:\\n results.append(result)\\n else:\\n print(f\"❌ Failed to process text {i+1}\")\\n\\n # Save results\\n with open(output_file, \\'w\\', encoding=\\'utf-8\\') as f:\\n for i, (input_text, result) in enumerate(zip(input_texts, results)):\\n f.write(f\"Input {i+1}: {input_text}\\\\n\")\\n f.write(f\"Output {i+1}: {result}\\\\n\")\\n f.write(\"-\" * 50 + \"\\\\n\")\\n\\n print(f\"✅ Batch inference completed! Results saved to: {output_file}\")\\n return True\\n\\ndef show_inference_features():\\n \"\"\"Show the features of the styling inference pipeline\"\"\"\\n print(\"=== Styling Inference Pipeline Features ===\")\\n print()\\n print(\"1. **Model Support**:\")\\n print(\" - Trained LoRA models from training pipeline\")\\n print(\" - Automatic model loading from config\")\\n print(\" - Native Unsloth inference optimization\")\\n print()\\n print(\"2. **Inference Modes**:\")\\n print(\" - Single text inference with instruction\")\\n print(\" - Batch file processing\")\\n print(\" - Streaming generation\")\\n print()\\n print(\"3. **Generation Control**:\")\\n print(\" - Configurable max tokens\")\\n print(\" - Same alpaca prompt format as training\")\\n print(\" - Automatic response extraction\")\\n print()\\n print(\"4. **Usage Examples**:\")\\n print(\" - Single inference: --instruction \\'style instruction\\' --input-text \\'your text\\'\")\\n print(\" - Streaming: add --stream flag\")\\n print(\" - Batch: use batch subcommand with input/output files\")\\n\\ndef create_inference_example():\\n \"\"\"Create an inference example using the formal style configuration\"\"\"\\n print(\"=== Inference Example: Formal Style Transfer ===\")\\n print()\\n\\n # Check if we have the required files\\n config_path = \"configs/styling/formal.yaml\"\\n\\n if not Path(config_path).exists():\\n print(f\"❌ Configuration file not found: {config_path}\")\\n print(\" Please run the data processor first to create the configuration\")\\n return False\\n\\n print(\"✅ Found configuration file!\")\\n print(f\" Config: {config_path}\")\\n print()\\n\\n # Example text\\n example_text = \"Hey, what\\'s up? I\\'m gonna go grab some food later.\"\\n\\n print(f\"Example text: {example_text}\")\\n print()\\n\\n # Run inference\\n success = run_inference_with_config(\\n config_path=config_path,\\n instruction=\"Rewrite the following text in a formal style\",\\n input_text=example_text\\n )\\n\\n if success:\\n print(\"✅ Example inference completed successfully!\")\\n return True\\n else:\\n print(\"❌ Example inference failed!\")\\n return False\\n\\ndef main():\\n \"\"\"Main inference function\"\"\"\\n parser = argparse.ArgumentParser(description=\"Styling Inference Pipeline\")\\n subparsers = parser.add_subparsers(dest=\"command\", help=\"Available commands\")\\n\\n # Inference command\\n infer_parser = subparsers.add_parser(\"infer\", help=\"Run single inference\")\\n infer_parser.add_argument(\"--config\", type=str, required=True, help=\"Path to YAML configuration file\")\\n infer_parser.add_argument(\"--instruction\", type=str, required=True, help=\"Style instruction\")\\n infer_parser.add_argument(\"--input-text\", type=str, default=\"\", help=\"Input text to style\")\\n infer_parser.add_argument(\"--max-tokens\", type=int, default=128, help=\"Maximum new tokens to generate\")\\n infer_parser.add_argument(\"--stream\", action=\"store_true\", help=\"Enable streaming generation\")\\n\\n # Batch inference command\\n batch_parser = subparsers.add_parser(\"batch\", help=\"Run batch inference\")\\n batch_parser.add_argument(\"--config\", type=str, required=True, help=\"Path to YAML configuration file\")\\n batch_parser.add_argument(\"--input-file\", type=str, required=True, help=\"Input file with texts to style\")\\n batch_parser.add_argument(\"--output-file\", type=str, required=True, help=\"Output file for styled texts\")\\n batch_parser.add_argument(\"--instruction\", type=str, required=True, help=\"Style instruction\")\\n batch_parser.add_argument(\"--max-tokens\", type=int, default=128, help=\"Maximum new tokens to generate\")\\n\\n # Features command\\n subparsers.add_parser(\"features\", help=\"Show available features\")\\n\\n # Example command\\n subparsers.add_parser(\"example\", help=\"Run example inference\")\\n\\n args = parser.parse_args()\\n\\n if args.command == \"infer\":\\n run_inference_with_config(\\n args.config, \\n args.instruction, \\n args.input_text, \\n args.max_tokens, \\n args.stream\\n )\\n elif args.command == \"batch\":\\n run_batch_inference_example(args.config, args.input_file, args.output_file, args.instruction, args.max_tokens)\\n elif args.command == \"features\":\\n show_inference_features()\\n elif args.command == \"example\":\\n create_inference_example()\\n else:\\n parser.print_help()\\n\\nif __name__ == \"__main__\":\\n main()\\n\\nERROR: \\n(base) macbook@DynamoAi fine-tune-task % python scripts/styling/inference.py\\nusage: inference.py [-h] {infer,batch,features,example} ...\\n\\nStyling Inference Pipeline\\n\\npositional arguments:\\n {infer,batch,features,example}\\n Available commands\\n infer Run single inference\\n batch Run batch inference\\n features Show available features\\n example Run example inference\\n\\noptions:\\n -h, --help show this help message and exit\\n(base) macbook@DynamoAi fine-tune-task %<|im_end|>\\n<|im_start|>assistant\\nThe error you encountered is due to the absence of a command argument when running the script `inference.py`. Your script is designed to be used in conjunction with various subcommands such as `infer`, `batch`, `features`, or `example`. \\n\\nHere\\'s how these subcommands work:\\n\\n1. `infer`:']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from unsloth.chat_templates import get_chat_template\n",
|
||
"\n",
|
||
"# Apply chat template to tokenizer\n",
|
||
"tokenizer = get_chat_template(\n",
|
||
" tokenizer,\n",
|
||
" chat_template=\"qwen-2.5\",\n",
|
||
")\n",
|
||
"\n",
|
||
"# Enable faster inference\n",
|
||
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
|
||
"\n",
|
||
"# Define the messages with properly formatted content\n",
|
||
"messages = [\n",
|
||
" {\n",
|
||
" \"role\": \"user\", \n",
|
||
" \"content\": \"\"\"Help me resolve the code: see the error I was getting\n",
|
||
"\n",
|
||
"#!/usr/bin/env python3\n",
|
||
"\\\"\\\"\\\"\n",
|
||
"Styling Inference Script\n",
|
||
"Provides a command-line interface to run the styling inference pipeline\n",
|
||
"\\\"\\\"\\\"\n",
|
||
"\n",
|
||
"import sys\n",
|
||
"import os\n",
|
||
"import subprocess\n",
|
||
"import argparse\n",
|
||
"from pathlib import Path\n",
|
||
"\n",
|
||
"def run_inference_with_config(config_path: str, instruction: str, input_text: str = \"\", max_tokens: int = 128, stream: bool = False):\n",
|
||
" \\\"\\\"\\\"Run inference using a YAML configuration file\\\"\\\"\\\"\n",
|
||
" print(f\"Running styling inference with config: {config_path}\")\n",
|
||
" print(f\"Instruction: {instruction}\")\n",
|
||
" print(f\"Input text: {input_text}\")\n",
|
||
" print(f\"Max tokens: {max_tokens}\")\n",
|
||
" print(f\"Streaming: {stream}\")\n",
|
||
" \n",
|
||
" cmd = [\n",
|
||
" \"python\", \"pipelines/styling/inference.py\",\n",
|
||
" \"--config\", config_path,\n",
|
||
" \"--instruction\", instruction,\n",
|
||
" \"--input-text\", input_text,\n",
|
||
" \"--max-tokens\", str(max_tokens)\n",
|
||
" ]\n",
|
||
" \n",
|
||
" if stream:\n",
|
||
" cmd.append(\"--stream\")\n",
|
||
" \n",
|
||
" print(f\"Running: {' '.join(cmd)}\")\n",
|
||
" \n",
|
||
" try:\n",
|
||
" result = subprocess.run(cmd, capture_output=True, text=True, check=True)\n",
|
||
" print(\"✅ Inference completed successfully!\")\n",
|
||
" print(\"Output:\")\n",
|
||
" print(result.stdout)\n",
|
||
" return result.stdout\n",
|
||
" except subprocess.CalledProcessError as e:\n",
|
||
" print(f\"❌ Inference failed: {e}\")\n",
|
||
" print(\"Error output:\")\n",
|
||
" print(e.stderr)\n",
|
||
" return None\n",
|
||
"\n",
|
||
"def run_batch_inference_example(config_path: str, input_file: str, output_file: str, instruction: str, max_tokens: int = 128):\n",
|
||
" \\\"\\\"\\\"Run batch inference example\\\"\\\"\\\"\n",
|
||
" print(f\"=== Batch Inference Example ===\")\n",
|
||
" print(f\"Config: {config_path}\")\n",
|
||
" print(f\"Input file: {input_file}\")\n",
|
||
" print(f\"Output file: {output_file}\")\n",
|
||
" print(f\"Instruction: {instruction}\")\n",
|
||
" print(f\"Max tokens: {max_tokens}\")\n",
|
||
" \n",
|
||
" # Check if input file exists\n",
|
||
" if not Path(input_file).exists():\n",
|
||
" print(f\"❌ Input file not found: {input_file}\")\n",
|
||
" return False\n",
|
||
" \n",
|
||
" # Read input texts\n",
|
||
" with open(input_file, 'r', encoding='utf-8') as f:\n",
|
||
" input_texts = [line.strip() for line in f if line.strip()]\n",
|
||
" \n",
|
||
" print(f\"Found {len(input_texts)} texts to process\")\n",
|
||
" \n",
|
||
" # Process each text\n",
|
||
" results = []\n",
|
||
" for i, text in enumerate(input_texts):\n",
|
||
" print(f\"\\\\nProcessing text {i+1}/{len(input_texts)}: {text[:50]}...\")\n",
|
||
" result = run_inference_with_config(config_path, instruction, text, max_tokens)\n",
|
||
" if result:\n",
|
||
" results.append(result)\n",
|
||
" else:\n",
|
||
" print(f\"❌ Failed to process text {i+1}\")\n",
|
||
" \n",
|
||
" # Save results\n",
|
||
" with open(output_file, 'w', encoding='utf-8') as f:\n",
|
||
" for i, (input_text, result) in enumerate(zip(input_texts, results)):\n",
|
||
" f.write(f\"Input {i+1}: {input_text}\\\\n\")\n",
|
||
" f.write(f\"Output {i+1}: {result}\\\\n\")\n",
|
||
" f.write(\"-\" * 50 + \"\\\\n\")\n",
|
||
" \n",
|
||
" print(f\"✅ Batch inference completed! Results saved to: {output_file}\")\n",
|
||
" return True\n",
|
||
"\n",
|
||
"def show_inference_features():\n",
|
||
" \\\"\\\"\\\"Show the features of the styling inference pipeline\\\"\\\"\\\"\n",
|
||
" print(\"=== Styling Inference Pipeline Features ===\")\n",
|
||
" print()\n",
|
||
" print(\"1. **Model Support**:\")\n",
|
||
" print(\" - Trained LoRA models from training pipeline\")\n",
|
||
" print(\" - Automatic model loading from config\")\n",
|
||
" print(\" - Native Unsloth inference optimization\")\n",
|
||
" print()\n",
|
||
" print(\"2. **Inference Modes**:\")\n",
|
||
" print(\" - Single text inference with instruction\")\n",
|
||
" print(\" - Batch file processing\")\n",
|
||
" print(\" - Streaming generation\")\n",
|
||
" print()\n",
|
||
" print(\"3. **Generation Control**:\")\n",
|
||
" print(\" - Configurable max tokens\")\n",
|
||
" print(\" - Same alpaca prompt format as training\")\n",
|
||
" print(\" - Automatic response extraction\")\n",
|
||
" print()\n",
|
||
" print(\"4. **Usage Examples**:\")\n",
|
||
" print(\" - Single inference: --instruction 'style instruction' --input-text 'your text'\")\n",
|
||
" print(\" - Streaming: add --stream flag\")\n",
|
||
" print(\" - Batch: use batch subcommand with input/output files\")\n",
|
||
"\n",
|
||
"def create_inference_example():\n",
|
||
" \\\"\\\"\\\"Create an inference example using the formal style configuration\\\"\\\"\\\"\n",
|
||
" print(\"=== Inference Example: Formal Style Transfer ===\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
" # Check if we have the required files\n",
|
||
" config_path = \"configs/styling/formal.yaml\"\n",
|
||
" \n",
|
||
" if not Path(config_path).exists():\n",
|
||
" print(f\"❌ Configuration file not found: {config_path}\")\n",
|
||
" print(\" Please run the data processor first to create the configuration\")\n",
|
||
" return False\n",
|
||
" \n",
|
||
" print(\"✅ Found configuration file!\")\n",
|
||
" print(f\" Config: {config_path}\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
" # Example text\n",
|
||
" example_text = \"Hey, what's up? I'm gonna go grab some food later.\"\n",
|
||
" \n",
|
||
" print(f\"Example text: {example_text}\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
" # Run inference\n",
|
||
" success = run_inference_with_config(\n",
|
||
" config_path=config_path,\n",
|
||
" instruction=\"Rewrite the following text in a formal style\",\n",
|
||
" input_text=example_text\n",
|
||
" )\n",
|
||
" \n",
|
||
" if success:\n",
|
||
" print(\"✅ Example inference completed successfully!\")\n",
|
||
" return True\n",
|
||
" else:\n",
|
||
" print(\"❌ Example inference failed!\")\n",
|
||
" return False\n",
|
||
"\n",
|
||
"def main():\n",
|
||
" \\\"\\\"\\\"Main inference function\\\"\\\"\\\"\n",
|
||
" parser = argparse.ArgumentParser(description=\"Styling Inference Pipeline\")\n",
|
||
" subparsers = parser.add_subparsers(dest=\"command\", help=\"Available commands\")\n",
|
||
" \n",
|
||
" # Inference command\n",
|
||
" infer_parser = subparsers.add_parser(\"infer\", help=\"Run single inference\")\n",
|
||
" infer_parser.add_argument(\"--config\", type=str, required=True, help=\"Path to YAML configuration file\")\n",
|
||
" infer_parser.add_argument(\"--instruction\", type=str, required=True, help=\"Style instruction\")\n",
|
||
" infer_parser.add_argument(\"--input-text\", type=str, default=\"\", help=\"Input text to style\")\n",
|
||
" infer_parser.add_argument(\"--max-tokens\", type=int, default=128, help=\"Maximum new tokens to generate\")\n",
|
||
" infer_parser.add_argument(\"--stream\", action=\"store_true\", help=\"Enable streaming generation\")\n",
|
||
" \n",
|
||
" # Batch inference command\n",
|
||
" batch_parser = subparsers.add_parser(\"batch\", help=\"Run batch inference\")\n",
|
||
" batch_parser.add_argument(\"--config\", type=str, required=True, help=\"Path to YAML configuration file\")\n",
|
||
" batch_parser.add_argument(\"--input-file\", type=str, required=True, help=\"Input file with texts to style\")\n",
|
||
" batch_parser.add_argument(\"--output-file\", type=str, required=True, help=\"Output file for styled texts\")\n",
|
||
" batch_parser.add_argument(\"--instruction\", type=str, required=True, help=\"Style instruction\")\n",
|
||
" batch_parser.add_argument(\"--max-tokens\", type=int, default=128, help=\"Maximum new tokens to generate\")\n",
|
||
" \n",
|
||
" # Features command\n",
|
||
" subparsers.add_parser(\"features\", help=\"Show available features\")\n",
|
||
" \n",
|
||
" # Example command\n",
|
||
" subparsers.add_parser(\"example\", help=\"Run example inference\")\n",
|
||
" \n",
|
||
" args = parser.parse_args()\n",
|
||
" \n",
|
||
" if args.command == \"infer\":\n",
|
||
" run_inference_with_config(\n",
|
||
" args.config, \n",
|
||
" args.instruction, \n",
|
||
" args.input_text, \n",
|
||
" args.max_tokens, \n",
|
||
" args.stream\n",
|
||
" )\n",
|
||
" elif args.command == \"batch\":\n",
|
||
" run_batch_inference_example(args.config, args.input_file, args.output_file, args.instruction, args.max_tokens)\n",
|
||
" elif args.command == \"features\":\n",
|
||
" show_inference_features()\n",
|
||
" elif args.command == \"example\":\n",
|
||
" create_inference_example()\n",
|
||
" else:\n",
|
||
" parser.print_help()\n",
|
||
"\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" main()\n",
|
||
"\n",
|
||
"ERROR: \n",
|
||
"(base) macbook@DynamoAi fine-tune-task % python scripts/styling/inference.py\n",
|
||
"usage: inference.py [-h] {infer,batch,features,example} ...\n",
|
||
"\n",
|
||
"Styling Inference Pipeline\n",
|
||
"\n",
|
||
"positional arguments:\n",
|
||
" {infer,batch,features,example}\n",
|
||
" Available commands\n",
|
||
" infer Run single inference\n",
|
||
" batch Run batch inference\n",
|
||
" features Show available features\n",
|
||
" example Run example inference\n",
|
||
"\n",
|
||
"options:\n",
|
||
" -h, --help show this help message and exit\n",
|
||
"(base) macbook@DynamoAi fine-tune-task %\"\"\"\n",
|
||
" },\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Apply chat template and tokenize\n",
|
||
"inputs = tokenizer.apply_chat_template(\n",
|
||
" messages,\n",
|
||
" tokenize=True,\n",
|
||
" add_generation_prompt=True, # Must add for generation\n",
|
||
" return_tensors=\"pt\",\n",
|
||
").to(\"cuda\")\n",
|
||
"\n",
|
||
"# Generate response\n",
|
||
"outputs = model.generate(\n",
|
||
" input_ids=inputs, \n",
|
||
" max_new_tokens=64, \n",
|
||
" use_cache=True,\n",
|
||
" temperature=1.5, \n",
|
||
" min_p=0.1\n",
|
||
")\n",
|
||
"\n",
|
||
"# Decode the outputs\n",
|
||
"result = tokenizer.batch_decode(outputs)\n",
|
||
"print(result)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "CrSvZObor0lY"
|
||
},
|
||
"source": [
|
||
" You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "e2pEuRb1r2Vg",
|
||
"outputId": "fe6590c1-dbd8-475b-c99b-17f88b302f9d"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"The error you're encountering is due to the fact that the script expects specific subcommands to be provided when running it. The error message indicates that no positional argument (subcommand) was given, which is required based on how the `argparse` module is set up in your script.\n",
|
||
"\n",
|
||
"### Explanation of the Error:\n",
|
||
"\n",
|
||
"When you run the script without any subcommands, the `args.command` will be `None`, leading to the `else` block where the help message is printed. This is why you see the usage information instead of the actual execution of the script.\n",
|
||
"\n",
|
||
"### Solution:\n",
|
||
"\n",
|
||
"To resolve this issue, you need to provide one of the expected subcommands when running the script. Here are the valid subcommands:\n",
|
||
"\n",
|
||
"1. **infer**: For running a single inference.\n",
|
||
"2. **batch**: For running batch inference.\n",
|
||
"3. **features**: To show the available features.\n",
|
||
"4. **example**: To run an example inference.\n",
|
||
"\n",
|
||
"### How to Run the Script Correctly:\n",
|
||
"\n",
|
||
"#### 1. Running Single Inference:\n",
|
||
"```bash\n",
|
||
"python scripts/styling/inference.py infer --config path/to/config.yaml --instruction \"Your instruction\" --input-text \"Your input text\"\n",
|
||
"```\n",
|
||
"\n",
|
||
"#### 2. Running Batch Inference:\n",
|
||
"```bash\n",
|
||
"python scripts/styling/inference.py batch --config path/to/config.yaml --input-file path/to/input.txt --output-file path/to/output.txt --instruction \"Your instruction\"\n",
|
||
"```\n",
|
||
"\n",
|
||
"#### 3. Showing Features:\n",
|
||
"```bash\n",
|
||
"python scripts/styling/inference.py features\n",
|
||
"```\n",
|
||
"\n",
|
||
"#### 4. Running Example Inference:\n",
|
||
"```bash\n",
|
||
"python scripts/styling/inference.py example\n",
|
||
"```\n",
|
||
"\n",
|
||
"### Additional Notes:\n",
|
||
"\n",
|
||
"- Ensure that the paths provided for `--config`, `--input-file`, and `--output-file` are correct and accessible.\n",
|
||
"- If you encounter any issues with file paths or configurations, check the existence of the files and the correctness of the YAML configuration.\n",
|
||
"\n",
|
||
"By providing the appropriate subcommand, you should be able to execute the script without encountering the error.<|im_end|>\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from unsloth.chat_templates import get_chat_template\n",
|
||
"\n",
|
||
"# Apply chat template to tokenizer\n",
|
||
"tokenizer = get_chat_template(\n",
|
||
" tokenizer,\n",
|
||
" chat_template=\"qwen-2.5\",\n",
|
||
")\n",
|
||
"\n",
|
||
"# Enable faster inference\n",
|
||
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
|
||
"\n",
|
||
"# Define the messages with properly formatted content\n",
|
||
"messages = [\n",
|
||
" {\n",
|
||
" \"role\": \"user\", \n",
|
||
" \"content\": \"\"\"Help me resolve the code: see the error I was getting\n",
|
||
"\n",
|
||
"#!/usr/bin/env python3\n",
|
||
"\\\"\\\"\\\"\n",
|
||
"Styling Inference Script\n",
|
||
"Provides a command-line interface to run the styling inference pipeline\n",
|
||
"\\\"\\\"\\\"\n",
|
||
"\n",
|
||
"import sys\n",
|
||
"import os\n",
|
||
"import subprocess\n",
|
||
"import argparse\n",
|
||
"from pathlib import Path\n",
|
||
"\n",
|
||
"def run_inference_with_config(config_path: str, instruction: str, input_text: str = \"\", max_tokens: int = 128, stream: bool = False):\n",
|
||
" \\\"\\\"\\\"Run inference using a YAML configuration file\\\"\\\"\\\"\n",
|
||
" print(f\"Running styling inference with config: {config_path}\")\n",
|
||
" print(f\"Instruction: {instruction}\")\n",
|
||
" print(f\"Input text: {input_text}\")\n",
|
||
" print(f\"Max tokens: {max_tokens}\")\n",
|
||
" print(f\"Streaming: {stream}\")\n",
|
||
" \n",
|
||
" cmd = [\n",
|
||
" \"python\", \"pipelines/styling/inference.py\",\n",
|
||
" \"--config\", config_path,\n",
|
||
" \"--instruction\", instruction,\n",
|
||
" \"--input-text\", input_text,\n",
|
||
" \"--max-tokens\", str(max_tokens)\n",
|
||
" ]\n",
|
||
" \n",
|
||
" if stream:\n",
|
||
" cmd.append(\"--stream\")\n",
|
||
" \n",
|
||
" print(f\"Running: {' '.join(cmd)}\")\n",
|
||
" \n",
|
||
" try:\n",
|
||
" result = subprocess.run(cmd, capture_output=True, text=True, check=True)\n",
|
||
" print(\"✅ Inference completed successfully!\")\n",
|
||
" print(\"Output:\")\n",
|
||
" print(result.stdout)\n",
|
||
" return result.stdout\n",
|
||
" except subprocess.CalledProcessError as e:\n",
|
||
" print(f\"❌ Inference failed: {e}\")\n",
|
||
" print(\"Error output:\")\n",
|
||
" print(e.stderr)\n",
|
||
" return None\n",
|
||
"\n",
|
||
"def run_batch_inference_example(config_path: str, input_file: str, output_file: str, instruction: str, max_tokens: int = 128):\n",
|
||
" \\\"\\\"\\\"Run batch inference example\\\"\\\"\\\"\n",
|
||
" print(f\"=== Batch Inference Example ===\")\n",
|
||
" print(f\"Config: {config_path}\")\n",
|
||
" print(f\"Input file: {input_file}\")\n",
|
||
" print(f\"Output file: {output_file}\")\n",
|
||
" print(f\"Instruction: {instruction}\")\n",
|
||
" print(f\"Max tokens: {max_tokens}\")\n",
|
||
" \n",
|
||
" # Check if input file exists\n",
|
||
" if not Path(input_file).exists():\n",
|
||
" print(f\"❌ Input file not found: {input_file}\")\n",
|
||
" return False\n",
|
||
" \n",
|
||
" # Read input texts\n",
|
||
" with open(input_file, 'r', encoding='utf-8') as f:\n",
|
||
" input_texts = [line.strip() for line in f if line.strip()]\n",
|
||
" \n",
|
||
" print(f\"Found {len(input_texts)} texts to process\")\n",
|
||
" \n",
|
||
" # Process each text\n",
|
||
" results = []\n",
|
||
" for i, text in enumerate(input_texts):\n",
|
||
" print(f\"\\\\nProcessing text {i+1}/{len(input_texts)}: {text[:50]}...\")\n",
|
||
" result = run_inference_with_config(config_path, instruction, text, max_tokens)\n",
|
||
" if result:\n",
|
||
" results.append(result)\n",
|
||
" else:\n",
|
||
" print(f\"❌ Failed to process text {i+1}\")\n",
|
||
" \n",
|
||
" # Save results\n",
|
||
" with open(output_file, 'w', encoding='utf-8') as f:\n",
|
||
" for i, (input_text, result) in enumerate(zip(input_texts, results)):\n",
|
||
" f.write(f\"Input {i+1}: {input_text}\\\\n\")\n",
|
||
" f.write(f\"Output {i+1}: {result}\\\\n\")\n",
|
||
" f.write(\"-\" * 50 + \"\\\\n\")\n",
|
||
" \n",
|
||
" print(f\"✅ Batch inference completed! Results saved to: {output_file}\")\n",
|
||
" return True\n",
|
||
"\n",
|
||
"def show_inference_features():\n",
|
||
" \\\"\\\"\\\"Show the features of the styling inference pipeline\\\"\\\"\\\"\n",
|
||
" print(\"=== Styling Inference Pipeline Features ===\")\n",
|
||
" print()\n",
|
||
" print(\"1. **Model Support**:\")\n",
|
||
" print(\" - Trained LoRA models from training pipeline\")\n",
|
||
" print(\" - Automatic model loading from config\")\n",
|
||
" print(\" - Native Unsloth inference optimization\")\n",
|
||
" print()\n",
|
||
" print(\"2. **Inference Modes**:\")\n",
|
||
" print(\" - Single text inference with instruction\")\n",
|
||
" print(\" - Batch file processing\")\n",
|
||
" print(\" - Streaming generation\")\n",
|
||
" print()\n",
|
||
" print(\"3. **Generation Control**:\")\n",
|
||
" print(\" - Configurable max tokens\")\n",
|
||
" print(\" - Same alpaca prompt format as training\")\n",
|
||
" print(\" - Automatic response extraction\")\n",
|
||
" print()\n",
|
||
" print(\"4. **Usage Examples**:\")\n",
|
||
" print(\" - Single inference: --instruction 'style instruction' --input-text 'your text'\")\n",
|
||
" print(\" - Streaming: add --stream flag\")\n",
|
||
" print(\" - Batch: use batch subcommand with input/output files\")\n",
|
||
"\n",
|
||
"def create_inference_example():\n",
|
||
" \\\"\\\"\\\"Create an inference example using the formal style configuration\\\"\\\"\\\"\n",
|
||
" print(\"=== Inference Example: Formal Style Transfer ===\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
" # Check if we have the required files\n",
|
||
" config_path = \"configs/styling/formal.yaml\"\n",
|
||
" \n",
|
||
" if not Path(config_path).exists():\n",
|
||
" print(f\"❌ Configuration file not found: {config_path}\")\n",
|
||
" print(\" Please run the data processor first to create the configuration\")\n",
|
||
" return False\n",
|
||
" \n",
|
||
" print(\"✅ Found configuration file!\")\n",
|
||
" print(f\" Config: {config_path}\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
" # Example text\n",
|
||
" example_text = \"Hey, what's up? I'm gonna go grab some food later.\"\n",
|
||
" \n",
|
||
" print(f\"Example text: {example_text}\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
" # Run inference\n",
|
||
" success = run_inference_with_config(\n",
|
||
" config_path=config_path,\n",
|
||
" instruction=\"Rewrite the following text in a formal style\",\n",
|
||
" input_text=example_text\n",
|
||
" )\n",
|
||
" \n",
|
||
" if success:\n",
|
||
" print(\"✅ Example inference completed successfully!\")\n",
|
||
" return True\n",
|
||
" else:\n",
|
||
" print(\"❌ Example inference failed!\")\n",
|
||
" return False\n",
|
||
"\n",
|
||
"def main():\n",
|
||
" \\\"\\\"\\\"Main inference function\\\"\\\"\\\"\n",
|
||
" parser = argparse.ArgumentParser(description=\"Styling Inference Pipeline\")\n",
|
||
" subparsers = parser.add_subparsers(dest=\"command\", help=\"Available commands\")\n",
|
||
" \n",
|
||
" # Inference command\n",
|
||
" infer_parser = subparsers.add_parser(\"infer\", help=\"Run single inference\")\n",
|
||
" infer_parser.add_argument(\"--config\", type=str, required=True, help=\"Path to YAML configuration file\")\n",
|
||
" infer_parser.add_argument(\"--instruction\", type=str, required=True, help=\"Style instruction\")\n",
|
||
" infer_parser.add_argument(\"--input-text\", type=str, default=\"\", help=\"Input text to style\")\n",
|
||
" infer_parser.add_argument(\"--max-tokens\", type=int, default=128, help=\"Maximum new tokens to generate\")\n",
|
||
" infer_parser.add_argument(\"--stream\", action=\"store_true\", help=\"Enable streaming generation\")\n",
|
||
" \n",
|
||
" # Batch inference command\n",
|
||
" batch_parser = subparsers.add_parser(\"batch\", help=\"Run batch inference\")\n",
|
||
" batch_parser.add_argument(\"--config\", type=str, required=True, help=\"Path to YAML configuration file\")\n",
|
||
" batch_parser.add_argument(\"--input-file\", type=str, required=True, help=\"Input file with texts to style\")\n",
|
||
" batch_parser.add_argument(\"--output-file\", type=str, required=True, help=\"Output file for styled texts\")\n",
|
||
" batch_parser.add_argument(\"--instruction\", type=str, required=True, help=\"Style instruction\")\n",
|
||
" batch_parser.add_argument(\"--max-tokens\", type=int, default=128, help=\"Maximum new tokens to generate\")\n",
|
||
" \n",
|
||
" # Features command\n",
|
||
" subparsers.add_parser(\"features\", help=\"Show available features\")\n",
|
||
" \n",
|
||
" # Example command\n",
|
||
" subparsers.add_parser(\"example\", help=\"Run example inference\")\n",
|
||
" \n",
|
||
" args = parser.parse_args()\n",
|
||
" \n",
|
||
" if args.command == \"infer\":\n",
|
||
" run_inference_with_config(\n",
|
||
" args.config, \n",
|
||
" args.instruction, \n",
|
||
" args.input_text, \n",
|
||
" args.max_tokens, \n",
|
||
" args.stream\n",
|
||
" )\n",
|
||
" elif args.command == \"batch\":\n",
|
||
" run_batch_inference_example(args.config, args.input_file, args.output_file, args.instruction, args.max_tokens)\n",
|
||
" elif args.command == \"features\":\n",
|
||
" show_inference_features()\n",
|
||
" elif args.command == \"example\":\n",
|
||
" create_inference_example()\n",
|
||
" else:\n",
|
||
" parser.print_help()\n",
|
||
"\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" main()\n",
|
||
"\n",
|
||
"ERROR: \n",
|
||
"(base) macbook@DynamoAi fine-tune-task % python scripts/styling/inference.py\n",
|
||
"usage: inference.py [-h] {infer,batch,features,example} ...\n",
|
||
"\n",
|
||
"Styling Inference Pipeline\n",
|
||
"\n",
|
||
"positional arguments:\n",
|
||
" {infer,batch,features,example}\n",
|
||
" Available commands\n",
|
||
" infer Run single inference\n",
|
||
" batch Run batch inference\n",
|
||
" features Show available features\n",
|
||
" example Run example inference\n",
|
||
"\n",
|
||
"options:\n",
|
||
" -h, --help show this help message and exit\n",
|
||
"(base) macbook@DynamoAi fine-tune-task %\"\"\"\n",
|
||
" },\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Apply chat template and tokenize\n",
|
||
"inputs = tokenizer.apply_chat_template(\n",
|
||
" messages,\n",
|
||
" tokenize=True,\n",
|
||
" add_generation_prompt=True, # Must add for generation\n",
|
||
" return_tensors=\"pt\",\n",
|
||
").to(\"cuda\")\n",
|
||
"\n",
|
||
"# Decode the outputs\n",
|
||
"result = tokenizer.batch_decode(outputs)\n",
|
||
"\n",
|
||
"from transformers import TextStreamer\n",
|
||
"text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
|
||
"_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 1024,\n",
|
||
" use_cache = True, temperature = 0.2, min_p = 0.1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
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"id": "uMuVrWbjAzhc"
|
||
},
|
||
"source": [
|
||
"<a name=\"Save\"></a>\n",
|
||
"### Saving, loading finetuned models\n",
|
||
"To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
|
||
"\n",
|
||
"**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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"execution_count": 27,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
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|
||
"id": "upcOlWe7A1vc",
|
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"outputId": "04d11b2b-7efd-4598-a60a-1873cfd3c431"
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|
||
{
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"model_id": "e60608a0567042ae9eef2a56b1760f96",
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"version_minor": 0
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{
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"data": {
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"model_id": "cd0250d7c7a9437b9428ee24d743c259",
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"version_major": 2,
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"text/plain": [
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{
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"data": {
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"model_id": "19242cb0878947259431e40d9499139a",
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"version_major": 2,
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "af4f5f965a28457398258d69ab48ee49",
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"text/plain": [
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" ..._research/adapter_model.safetensors: 9%|8 | 75.5MB / 842MB "
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},
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"metadata": {},
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"output_type": "display_data"
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},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saved model to https://huggingface.co/mkddatascience/qwen_2_5_72b_debug_research\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "3c09b2b23d6843c6862a7f7f35a9c2a1",
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "cfffa42f622844dc8fa9bfb1ef0e44f1",
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"version_major": 2,
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ee1cb5de1f88432db20b346669d617ba",
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"version_major": 2,
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"text/plain": [
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" ...5_72b_debug_research/tokenizer.json: 100%|##########| 11.4MB / 11.4MB "
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},
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||
"metadata": {},
|
||
"output_type": "display_data"
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||
}
|
||
],
|
||
"source": [
|
||
"model.save_pretrained(\"qwen_2_5_72b_debug_research\") # Local saving\n",
|
||
"tokenizer.save_pretrained(\"qwen_2_5_72b_debug_research\")\n",
|
||
"model.push_to_hub(\"mkddatascience/qwen_2_5_72b_debug_research\", token = \"hf_AKaOCXoFxJiTvQFVtrdfrQcWeJe.....\") # Online saving\n",
|
||
"tokenizer.push_to_hub(\"mkddatascience/qwen_2_5_72b_debug_research\", token = \"hf_AKaOCXoFxJiTvQFVtrdfrQcWe.....\") # Online saving"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "AEEcJ4qfC7Lp"
|
||
},
|
||
"source": [
|
||
"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "MKX_XKs_BNZR",
|
||
"outputId": "8a312de6-f0f5-43d2-9635-cbe65ff81982"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"The most iconic and well-known tall tower in Paris, the capital of France, is the Eiffel Tower. It's a marvel of late 19th-century engineering and an enduring symbol of not only Paris but also of France itself.\n",
|
||
"\n",
|
||
"**Construction and History**\n",
|
||
"Built for the "
|
||
]
|
||
},
|
||
{
|
||
"ename": "KeyboardInterrupt",
|
||
"evalue": "",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||
"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 23\u001b[39m\n\u001b[32m 21\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtransformers\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TextStreamer\n\u001b[32m 22\u001b[39m text_streamer = TextStreamer(tokenizer, skip_prompt = \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m---> \u001b[39m\u001b[32m23\u001b[39m _ = \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstreamer\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext_streamer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m128\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 24\u001b[39m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_p\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0.1\u001b[39;49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/peft/peft_model.py:1973\u001b[39m, in \u001b[36mPeftModelForCausalLM.generate\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1971\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._enable_peft_forward_hooks(*args, **kwargs):\n\u001b[32m 1972\u001b[39m kwargs = {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs.items() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.special_peft_forward_args}\n\u001b[32m-> \u001b[39m\u001b[32m1973\u001b[39m outputs = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbase_model\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1974\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1975\u001b[39m outputs = \u001b[38;5;28mself\u001b[39m.base_model.generate(**kwargs)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/unsloth/models/llama.py:1794\u001b[39m, in \u001b[36munsloth_fast_generate\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1792\u001b[39m \u001b[38;5;66;03m# Mixed precision autocast\u001b[39;00m\n\u001b[32m 1793\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m torch.inference_mode(), torch.autocast(device_type = DEVICE_TYPE, dtype = dtype):\n\u001b[32m-> \u001b[39m\u001b[32m1794\u001b[39m output = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_old_generate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1795\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 1797\u001b[39m \u001b[38;5;66;03m# Return accelerate back\u001b[39;00m\n\u001b[32m 1798\u001b[39m \u001b[38;5;66;03m# if accelerate_new_send_to_device is not None:\u001b[39;00m\n\u001b[32m 1799\u001b[39m \u001b[38;5;66;03m# accelerate.utils.operations.send_to_device = accelerate_old_send_to_device\u001b[39;00m\n\u001b[32m 1800\u001b[39m \u001b[38;5;66;03m# pass\u001b[39;00m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py:120\u001b[39m, in \u001b[36mcontext_decorator.<locals>.decorate_context\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 117\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(func)\n\u001b[32m 118\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdecorate_context\u001b[39m(*args, **kwargs):\n\u001b[32m 119\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[32m--> \u001b[39m\u001b[32m120\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py:2617\u001b[39m, in \u001b[36mGenerationMixin.generate\u001b[39m\u001b[34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, use_model_defaults, custom_generate, **kwargs)\u001b[39m\n\u001b[32m 2609\u001b[39m input_ids, model_kwargs = \u001b[38;5;28mself\u001b[39m._expand_inputs_for_generation(\n\u001b[32m 2610\u001b[39m input_ids=input_ids,\n\u001b[32m 2611\u001b[39m expand_size=generation_config.num_return_sequences,\n\u001b[32m 2612\u001b[39m is_encoder_decoder=\u001b[38;5;28mself\u001b[39m.config.is_encoder_decoder,\n\u001b[32m 2613\u001b[39m **model_kwargs,\n\u001b[32m 2614\u001b[39m )\n\u001b[32m 2616\u001b[39m \u001b[38;5;66;03m# 12. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2617\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2618\u001b[39m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2619\u001b[39m \u001b[43m \u001b[49m\u001b[43mlogits_processor\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprepared_logits_processor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2620\u001b[39m \u001b[43m \u001b[49m\u001b[43mstopping_criteria\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprepared_stopping_criteria\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2621\u001b[39m \u001b[43m \u001b[49m\u001b[43mgeneration_config\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgeneration_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2622\u001b[39m \u001b[43m \u001b[49m\u001b[43msynced_gpus\u001b[49m\u001b[43m=\u001b[49m\u001b[43msynced_gpus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2623\u001b[39m \u001b[43m \u001b[49m\u001b[43mstreamer\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstreamer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2624\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2625\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2627\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m generation_mode \u001b[38;5;129;01min\u001b[39;00m (GenerationMode.BEAM_SAMPLE, GenerationMode.BEAM_SEARCH):\n\u001b[32m 2628\u001b[39m \u001b[38;5;66;03m# 11. interleave input_ids with `num_beams` additional sequences per batch\u001b[39;00m\n\u001b[32m 2629\u001b[39m input_ids, model_kwargs = \u001b[38;5;28mself\u001b[39m._expand_inputs_for_generation(\n\u001b[32m 2630\u001b[39m input_ids=input_ids,\n\u001b[32m 2631\u001b[39m expand_size=generation_config.num_beams,\n\u001b[32m 2632\u001b[39m is_encoder_decoder=\u001b[38;5;28mself\u001b[39m.config.is_encoder_decoder,\n\u001b[32m 2633\u001b[39m **model_kwargs,\n\u001b[32m 2634\u001b[39m )\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py:3601\u001b[39m, in \u001b[36mGenerationMixin._sample\u001b[39m\u001b[34m(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, **model_kwargs)\u001b[39m\n\u001b[32m 3599\u001b[39m is_prefill = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 3600\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3601\u001b[39m outputs = \u001b[43mmodel_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 3603\u001b[39m \u001b[38;5;66;03m# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping\u001b[39;00m\n\u001b[32m 3604\u001b[39m model_kwargs = \u001b[38;5;28mself\u001b[39m._update_model_kwargs_for_generation(\n\u001b[32m 3605\u001b[39m outputs,\n\u001b[32m 3606\u001b[39m model_kwargs,\n\u001b[32m 3607\u001b[39m is_encoder_decoder=\u001b[38;5;28mself\u001b[39m.config.is_encoder_decoder,\n\u001b[32m 3608\u001b[39m )\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py:1773\u001b[39m, in \u001b[36mModule._wrapped_call_impl\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1771\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._compiled_call_impl(*args, **kwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[32m 1772\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1773\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py:1784\u001b[39m, in \u001b[36mModule._call_impl\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 1779\u001b[39m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[32m 1780\u001b[39m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[32m 1781\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m._backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m._forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m._forward_pre_hooks\n\u001b[32m 1782\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[32m 1783\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[32m-> \u001b[39m\u001b[32m1784\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1786\u001b[39m result = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1787\u001b[39m called_always_called_hooks = \u001b[38;5;28mset\u001b[39m()\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/unsloth/models/llama.py:1166\u001b[39m, in \u001b[36mCausalLM_fast_forward.<locals>._CausalLM_fast_forward\u001b[39m\u001b[34m(self, input_ids, causal_mask, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, num_logits_to_keep, logits_to_keep, *args, **kwargs)\u001b[39m\n\u001b[32m 1148\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_CausalLM_fast_forward\u001b[39m(\n\u001b[32m 1149\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1150\u001b[39m input_ids: torch.LongTensor = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1163\u001b[39m *args, **kwargs,\n\u001b[32m 1164\u001b[39m ) -> Union[Tuple, CausalLMOutputWithPast]:\n\u001b[32m 1165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m past_key_values \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1166\u001b[39m outputs = \u001b[43mfast_forward_inference\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1167\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 1168\u001b[39m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1169\u001b[39m \u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1170\u001b[39m \u001b[43m \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1171\u001b[39m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1172\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1173\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1174\u001b[39m causal_mask = xformers.attn_bias.LowerTriangularMask() \u001b[38;5;28;01mif\u001b[39;00m HAS_XFORMERS \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/unsloth/models/llama.py:1117\u001b[39m, in \u001b[36m_LlamaModel_fast_forward_inference.<locals>.LlamaModel_fast_forward_inference_custom\u001b[39m\u001b[34m(self, input_ids, past_key_values, position_ids, attention_mask)\u001b[39m\n\u001b[32m 1109\u001b[39m residual.copy_(X) \u001b[38;5;66;03m# residual = X\u001b[39;00m\n\u001b[32m 1110\u001b[39m X = fast_rms_layernorm_inference(\n\u001b[32m 1111\u001b[39m decoder_layer.post_attention_layernorm,\n\u001b[32m 1112\u001b[39m X,\n\u001b[32m (...)\u001b[39m\u001b[32m 1115\u001b[39m variance = variance,\n\u001b[32m 1116\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1117\u001b[39m X = \u001b[43mmlp_fast_forward_inference\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1118\u001b[39m \u001b[43m \u001b[49m\u001b[43mdecoder_layer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmlp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1119\u001b[39m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1120\u001b[39m \u001b[43m \u001b[49m\u001b[43mtemp_gate\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemp_gates\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdevice_index\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1121\u001b[39m \u001b[43m \u001b[49m\u001b[43mtemp_up\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemp_ups\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdevice_index\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1122\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1123\u001b[39m X += residual\n\u001b[32m 1125\u001b[39m next_decoder_cache.append(present_key_value)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/unsloth/models/llama.py:411\u001b[39m, in \u001b[36mfast_swiglu_inference\u001b[39m\u001b[34m(self, X, temp_gate, temp_up, gate_multiplier, down_multiplier)\u001b[39m\n\u001b[32m 408\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m gate_multiplier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 409\u001b[39m gate *= gate_multiplier\n\u001b[32m--> \u001b[39m\u001b[32m411\u001b[39m up = \u001b[43mfast_linear_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m \u001b[49m\u001b[43mup_proj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemp_up\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 413\u001b[39m gate = torch_nn_functional_silu(gate, inplace = \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 414\u001b[39m gate *= up\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/usr/local/lib/python3.11/dist-packages/unsloth/kernels/utils.py:680\u001b[39m, in \u001b[36mfast_linear_forward\u001b[39m\u001b[34m(proj, X, temp_lora, out)\u001b[39m\n\u001b[32m 678\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m bsz == \u001b[32m1\u001b[39m:\n\u001b[32m 679\u001b[39m out = out.view(out_dim)\n\u001b[32m--> \u001b[39m\u001b[32m680\u001b[39m temp_lora = \u001b[43mtorch_mv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlora_A\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_fast_lora\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m.\u001b[49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemp_lora\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 681\u001b[39m out.addmv_(lora_B._fast_lora, temp_lora, alpha = lora_S)\n\u001b[32m 682\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
|
||
"\u001b[31mKeyboardInterrupt\u001b[39m: "
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if False:\n",
|
||
" from unsloth import FastLanguageModel\n",
|
||
" model, tokenizer = FastLanguageModel.from_pretrained(\n",
|
||
" model_name = \"qwen_2_5_72b_debug_research\", # YOUR MODEL YOU USED FOR TRAINING\n",
|
||
" max_seq_length = max_seq_length,\n",
|
||
" dtype = dtype,\n",
|
||
" load_in_4bit = load_in_4bit,\n",
|
||
" )\n",
|
||
" FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
|
||
"\n",
|
||
"messages = [\n",
|
||
" {\"role\": \"user\", \"content\": \"Describe a tall tower in the capital of France.\"},\n",
|
||
"]\n",
|
||
"inputs = tokenizer.apply_chat_template(\n",
|
||
" messages,\n",
|
||
" tokenize = True,\n",
|
||
" add_generation_prompt = True, # Must add for generation\n",
|
||
" return_tensors = \"pt\",\n",
|
||
").to(\"cuda\")\n",
|
||
"\n",
|
||
"from transformers import TextStreamer\n",
|
||
"text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
|
||
"_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,\n",
|
||
" use_cache = True, temperature = 1.5, min_p = 0.1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "QQMjaNrjsU5_"
|
||
},
|
||
"source": [
|
||
"You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "yFfaXG0WsQuE"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"if False:\n",
|
||
" # I highly do NOT suggest - use Unsloth if possible\n",
|
||
" from peft import AutoPeftModelForCausalLM\n",
|
||
" from transformers import AutoTokenizer\n",
|
||
"\n",
|
||
" model = AutoPeftModelForCausalLM.from_pretrained(\n",
|
||
" \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
|
||
" load_in_4bit=load_in_4bit,\n",
|
||
" )\n",
|
||
" tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "f422JgM9sdVT"
|
||
},
|
||
"source": [
|
||
"### Saving to float16 for VLLM\n",
|
||
"\n",
|
||
"We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "iHjt_SMYsd3P"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Merge to 16bit\n",
|
||
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
|
||
"if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
|
||
"\n",
|
||
"# Merge to 4bit\n",
|
||
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
|
||
"if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
|
||
"\n",
|
||
"# Just LoRA adapters\n",
|
||
"if False:\n",
|
||
" model.save_pretrained(\"model\")\n",
|
||
" tokenizer.save_pretrained(\"model\")\n",
|
||
"if False:\n",
|
||
" model.push_to_hub(\"hf/model\", token = \"\")\n",
|
||
" tokenizer.push_to_hub(\"hf/model\", token = \"\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "TCv4vXHd61i7"
|
||
},
|
||
"source": [
|
||
"### GGUF / llama.cpp Conversion\n",
|
||
"To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
|
||
"\n",
|
||
"Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
|
||
"* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
|
||
"* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
|
||
"* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n",
|
||
"\n",
|
||
"[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "FqfebeAdT073"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Save to 8bit Q8_0\n",
|
||
"if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
|
||
"# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
|
||
"# And change hf to your username!\n",
|
||
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
|
||
"\n",
|
||
"# Save to 16bit GGUF\n",
|
||
"if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
|
||
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
|
||
"\n",
|
||
"# Save to q4_k_m GGUF\n",
|
||
"if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
|
||
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n",
|
||
"\n",
|
||
"# Save to multiple GGUF options - much faster if you want multiple!\n",
|
||
"if False:\n",
|
||
" model.push_to_hub_gguf(\n",
|
||
" \"hf/model\", # Change hf to your username!\n",
|
||
" tokenizer,\n",
|
||
" quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
|
||
" token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "kBvrJqNr6L9g"
|
||
},
|
||
"source": [
|
||
"Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp.\n",
|
||
"\n",
|
||
"And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
|
||
"\n",
|
||
"Some other links:\n",
|
||
"1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
|
||
"2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
|
||
"3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
|
||
"6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
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"\n",
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|
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"\n",
|
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" Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n",
|
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