<environment_context>
  <cwd>/root</cwd>
  <approval_policy>on-request</approval_policy>
  <sandbox_mode>workspace-write</sandbox_mode>
  <network_access>restricted</network_access>
  <shell>zsh</shell>
</environment_context>


hello


Summary: auto


**Preparing greeting response**


Hi there!


gh clone 爬虫相关的项目


**Clarifying GitHub clone intent**


我手头没有具体的爬虫项目仓库信息可以克隆。能否提供 GitHub 上的具体仓库名或 URL？


推荐机器学习项目


**Gathering repository recommendations**


这里有几个值得关注的机器学习项目，涵盖入门到工程实践：

- `scikit-learn/scikit-learn`：经典的 Python 机器学习库，适合掌握算法实现与 API 设计 (`github.com/scikit-learn/scikit-learn`).
- `pytorch/pytorch`：主流深度学习框架，学习底层实现和算子机制 (`github.com/pytorch/pytorch`).
- `tensorflow/models`：Google 开源的 TensorFlow 模型集合，提供完整训练/部署示例 (`github.com/tensorflow/models`).
- `huggingface/transformers`：自然语言处理的主力库，涵盖 Transformers 模型与推理工具 (`github.com/huggingface/transformers`).
- `keras-team/keras`：高层深度学习 API，适合快速原型和教学 (`github.com/keras-team/keras`).

如果你聚焦在特定领域（如 CV、推荐、强化学习等），告诉我我可以再细化推荐。


克隆huggingface/transformers


**Preparing to clone repository**


Output:
failed in sandbox: Cloning into 'transformers'...
fatal: unable to access 'https://github.com/huggingface/transformers.git/': Could not resolve host: github.com


shell

Need network access to clone the requested GitHub repository


Output: {"output":"command timed out after 10021 milliseconds\nCloning into 'transformers'...\nfatal: early EOF\n","metadata":{"exit_code":124,"duration_seconds":10.0}}


**Considering shallow clone to avoid timeout**


git clone --depth 1 https://github.com/huggingface/transformers.git


Output: {"output":"fatal: destination path 'transformers' already exists and is not an empty directory.\n","metadata":{"exit_code":128,"duration_seconds":0.0}}


**Deciding on cleanup of partial folder**


Output: {"output":"","metadata":{"exit_code":0,"duration_seconds":0.1}}


**Cloning repository shallowly**


Output: {"output":"Cloning into 'transformers'...\nUpdating files:  81% (4213/5166)\rUpdating files:  82% (4237/5166)\rUpdating files:  83% (4288/5166)\rUpdating files:  84% (4340/5166)\rUpdating files:  85% (4392/5166)\rUpdating files:  86% (4443/5166)\rUpdating files:  87% (4495/5166)\rUpdating files:  88% (4547/5166)\rUpdating files:  89% (4598/5166)\rUpdating files:  90% (4650/5166)\rUpdating files:  91% (4702/5166)\rUpdating files:  92% (4753/5166)\rUpdating files:  93% (4805/5166)\rUpdating files:  94% (4857/5166)\rUpdating files:  95% (4908/5166)\rUpdating files:  96% (4960/5166)\rUpdating files:  97% (5012/5166)\rUpdating files:  98% (5063/5166)\rUpdating files:  99% (5115/5166)\rUpdating files: 100% (5166/5166)\rUpdating files: 100% (5166/5166), done.\n","metadata":{"exit_code":0,"duration_seconds":5.7}}


**Preparing final confirmation**


`huggingface/transformers` is now cloned under `/root/transformers` (shallow `--depth 1` to avoid timeouts). Let me know if you’d like to explore or run anything in the repo.


!cd /root/transformersgo on


Summary: auto


**Checking current status**


Output: {"output":"/root/transformers\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Considering next steps**


**Considering response necessity**


`/root/transformers` is set as the working directory; project contents are ready (docs, src, examples, tests, etc.). Tell me what you’d like to do next—build, run tests, inspect files?


!pwd


Output: {"output":"/root/transformers\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


当前目录是 `/root/transformers`。需要查看或运行什么可以告诉我。


!ls


仓库内容已列出（`README.md`, `src`, `examples`, `tests`, 等）。需要我查看或运行什么操作？


生成项目说明


**Preparing to summarize project from README**


Output:
{"output":"\n\n
\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg\">\n    <source media=\"(prefers-color-scheme: light)\" srcset=\"https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg\">\n    \n  </picture>\n
\n
\n
\n\n
\n    \n    \n    \n    \n    \n    \n    \n
\n\n
\n
\n        English |\n        简体中文 |\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Planning deeper README review**


rg -n "Transformers provides" -n README.md


Output: {"output":"","metadata":{"exit_code":1,"duration_seconds":0.0}}


**Considering reading more lines**


Output:
{"output":"Explore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.\n\n## Installation\n\nTransformers works with Python 3.9+, and [PyTorch](https://pytorch.org/get-started/locally/) 2.1+.\n\nCreate and activate a virtual environment with [venv](https://docs.python.org/3/library/venv.html) or [uv](https://docs.astral.sh/uv/), a fast Rust-based Python package and project manager.\n\n```py\n# venv\npython -m venv .my-env\nsource .my-env/bin/activate\n# uv\nuv venv .my-env\nsource .my-env/bin/activate\n```\n\nInstall Transformers in your virtual environment.\n\n```py\n# pip\npip install \"transformers[torch]\"\n\n# uv\nuv pip install \"transformers[torch]\"\n```\n\nInstall Transformers from source if you want the latest changes in the library or are interested in contributing. However, the *latest* version may not be stable. Feel free to open an [issue](https://github.com/huggingface/transformers/issues) if you encounter an error.\n\n```shell\ngit clone https://github.com/huggingface/transformers.git\ncd transformers\n\n# pip\npip install '.[torch]'\n\n# uv\nuv pip install '.[torch]'\n```\n\n## Quickstart\n\nGet started with Transformers right away with the [Pipeline](https://huggingface.co/docs/transformers/pipeline_tutorial) API. The `Pipeline` is a high-level inference class that supports text, audio, vision, and multimodal tasks. It handles preprocessing the input and returns the appropriate output.\n\nInstantiate a pipeline and specify model to use for text generation. The model is downloaded and cached so you can easily reuse it again. Finally, pass some text to prompt the model.\n\n```py\nfrom transformers import pipeline\n\npipeline = pipeline(task=\"text-generation\", model=\"Qwen/Qwen2.5-1.5B\")\npipeline(\"the secret to baking a really good cake is \")\n[{'generated_text': 'the secret to baking a really good cake is 1) to use the right ingredients and 2) to follow the recipe exactly. the recipe for the cake is as follows: 1 cup of sugar, 1 cup of flour, 1 cup of milk, 1 cup of butter, 1 cup of eggs, 1 cup of chocolate chips. if you want to make 2 cakes, how much sugar do you need? To make 2 cakes, you will need 2 cups of sugar.'}]\n```\n\nTo chat with a model, the usage pattern is the same. The only difference is you need to construct a chat history (the input to `Pipeline`) between you and the system.\n\n> [!TIP]\n> You can also chat with a model directly from the command line, as long as [`transformers serve` is running](https://huggingface.co/docs/transformers/main/en/serving).\n> ```shell\n> transformers chat Qwen/Qwen2.5-0.5B-Instruct\n> ```\n\n```py\nimport torch\nfrom transformers import pipeline\n\nchat = [\n    {\"role\": \"system\", \"content\": \"You are a sassy, wise-cracking robot as imagined by Hollywood circa 1986.\"},\n    {\"role\": \"user\", \"content\": \"Hey, can you tell me any fun things to do in New York?\"}\n]\n\npipeline = pipeline(task=\"text-generation\", model=\"meta-llama/Meta-Llama-3-8B-Instruct\", dtype=torch.bfloat16, device_map=\"auto\")\nresponse = pipeline(chat, max_new_tokens=512)\nprint(response[0][\"generated_text\"][-1][\"content\"])\n```\n\nExpand the examples below to see how `Pipeline` works for different modalities and tasks.\n\n\nAutomatic speech recognition\n\n```py\nfrom transformers import pipeline\n\npipeline = pipeline(task=\"automatic-speech-recognition\", model=\"openai/whisper-large-v3\")\npipeline(\"https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac\")\n{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}\n```\n\n\n\n\nImage classification\n\n
\n    \n
\n\n```py\nfrom transformers import pipeline\n\npipeline = pipeline(task=\"image-classification\", model=\"facebook/dinov2-small-imagenet1k-1-layer\")\npipeline(\"https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png\")\n[{'label': 'macaw', 'score': 0.997848391532898},\n {'label': 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',\n  'score': 0.0016551691805943847},\n {'label': 'lorikeet', 'score': 0.00018523589824326336},\n {'label': 'African grey, African gray, Psittacus erithacus',\n  'score': 7.85409429227002e-05},\n {'label': 'quail', 'score': 5.502637941390276e-05}]\n```\n\n\n\n\nVisual question answering\n\n
\n    \n
\n\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Preparing detailed repo description**


bash
-lc
nl -ba README.md | sed -n '70,160p'


Output: {"output":"    70\tIt centralizes the model definition so that this definition is agreed upon across the ecosystem. `transformers` is the\n    71\tpivot across frameworks: if a model definition is supported, it will be compatible with the majority of training\n    72\tframeworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),\n    73\tand adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition from `transformers`.\n    74\t\n    75\tWe pledge to help support new state-of-the-art models and democratize their usage by having their model definition be\n    76\tsimple, customizable, and efficient.\n    77\t\n    78\tThere are over 1M+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.\n    79\t\n    80\tExplore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.\n    81\t\n    82\t## Installation\n    83\t\n    84\tTransformers works with Python 3.9+, and [PyTorch](https://pytorch.org/get-started/locally/) 2.1+.\n    85\t\n    86\tCreate and activate a virtual environment with [venv](https://docs.python.org/3/library/venv.html) or [uv](https://docs.astral.sh/uv/), a fast Rust-based Python package and project manager.\n    87\t\n    88\t```py\n    89\t# venv\n    90\tpython -m venv .my-env\n    91\tsource .my-env/bin/activate\n    92\t# uv\n    93\tuv venv .my-env\n    94\tsource .my-env/bin/activate\n    95\t```\n    96\t\n    97\tInstall Transformers in your virtual environment.\n    98\t\n    99\t```py\n   100\t# pip\n   101\tpip install \"transformers[torch]\"\n   102\t\n   103\t# uv\n   104\tuv pip install \"transformers[torch]\"\n   105\t```\n   106\t\n   107\tInstall Transformers from source if you want the latest changes in the library or are interested in contributing. However, the *latest* version may not be stable. Feel free to open an [issue](https://github.com/huggingface/transformers/issues) if you encounter an error.\n   108\t\n   109\t```shell\n   110\tgit clone https://github.com/huggingface/transformers.git\n   111\tcd transformers\n   112\t\n   113\t# pip\n   114\tpip install '.[torch]'\n   115\t\n   116\t# uv\n   117\tuv pip install '.[torch]'\n   118\t```\n   119\t\n   120\t## Quickstart\n   121\t\n   122\tGet started with Transformers right away with the [Pipeline](https://huggingface.co/docs/transformers/pipeline_tutorial) API. The `Pipeline` is a high-level inference class that supports text, audio, vision, and multimodal tasks. It handles preprocessing the input and returns the appropriate output.\n   123\t\n   124\tInstantiate a pipeline and specify model to use for text generation. The model is downloaded and cached so you can easily reuse it again. Finally, pass some text to prompt the model.\n   125\t\n   126\t```py\n   127\tfrom transformers import pipeline\n   128\t\n   129\tpipeline = pipeline(task=\"text-generation\", model=\"Qwen/Qwen2.5-1.5B\")\n   130\tpipeline(\"the secret to baking a really good cake is \")\n   131\t[{'generated_text': 'the secret to baking a really good cake is 1) to use the right ingredients and 2) to follow the recipe exactly. the recipe for the cake is as follows: 1 cup of sugar, 1 cup of flour, 1 cup of milk, 1 cup of butter, 1 cup of eggs, 1 cup of chocolate chips. if you want to make 2 cakes, how much sugar do you need? To make 2 cakes, you will need 2 cups of sugar.'}]\n   132\t```\n   133\t\n   134\tTo chat with a model, the usage pattern is the same. The only difference is you need to construct a chat history (the input to `Pipeline`) between you and the system.\n   135\t\n   136\t> [!TIP]\n   137\t> You can also chat with a model directly from the command line, as long as [`transformers serve` is running](https://huggingface.co/docs/transformers/main/en/serving).\n   138\t> ```shell\n   139\t> transformers chat Qwen/Qwen2.5-0.5B-Instruct\n   140\t> ```\n   141\t\n   142\t```py\n   143\timport torch\n   144\tfrom transformers import pipeline\n   145\t\n   146\tchat = [\n   147\t    {\"role\": \"system\", \"content\": \"You are a sassy, wise-cracking robot as imagined by Hollywood circa 1986.\"},\n   148\t    {\"role\": \"user\", \"content\": \"Hey, can you tell me any fun things to do in New York?\"}\n   149\t]\n   150\t\n   151\tpipeline = pipeline(task=\"text-generation\", model=\"meta-llama/Meta-Llama-3-8B-Instruct\", dtype=torch.bfloat16, device_map=\"auto\")\n   152\tresponse = pipeline(chat, max_new_tokens=512)\n   153\tprint(response[0][\"generated_text\"][-1][\"content\"])\n   154\t```\n   155\t\n   156\tExpand the examples below to see how `Pipeline` works for different modalities and tasks.\n   157\t\n   158\t<details>\n   159\t<summary>Automatic speech recognition</summary>\n   160\t\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Summarizing key README features**


**Project Overview**
- Hugging Face `transformers` centralizes canonical model definitions so the same configs run across popular training libraries (Axolotl, DeepSpeed, Lightning), inference stacks (vLLM, SGLang, TGI) and lightweight runtimes (`llama.cpp`, `mlx`), with 1M+ compatible checkpoints on the Hub for immediate reuse (`README.md:70-80`).
- Installing targets Python 3.9+ and PyTorch 2.1+; the README walks through creating a `venv`/`uv` environment and either pip-installing the released wheel (`pip install "transformers[torch]"`) or cloning the repo for `pip install '.[torch]'` (`README.md:84-118`).
- Quickstart usage revolves around the high-level `pipeline` API that handles preprocessing and batching for text, audio, vision, and multimodal tasks; the README demonstrates text generation and chat orchestration, plus expandable samples for ASR, image classification, and VQA (`README.md:122-160`).
- The repo is organized for both library development (`src/transformers`), runnable demos (`examples`, `notebooks`, `benchmark*`), docs (`docs`, `i18n` for localized READMEs), packaging (`pyproject.toml`, `setup.py`, `Makefile`), and contributor guidance (`CONTRIBUTING.md`, `MIGRATION_GUIDE_V5.md`, `AGENTS.md`), making it easy to explore any layer of the stack.

Natural next steps could be (1) installing editable deps to run local experiments, (2) browsing `examples` for task-specific training scripts, or (3) reading `docs/source/en/index.mdx` if you need a documentation entry point—happy to help with any of those.


Summary: auto


项目结构同前：核心源码在 `src`, 示例脚本在 `examples`, 文档在 `docs/i18n`, 还有基准、notebooks、测试等子目录。想深入某块请告诉我。


分析src目录


**Listing project structure contents**


Output: {"output":"transformers\n","metadata":{"exit_code":0,"duration_seconds":3.2}}


**Listing key modules and subdirectories**


bash
-lc
ls -d src/transformers/*/ 2>/dev/null


**Planning file referencing details**


bash
-lc
ls src/transformers | sed -n '1,120p'


**Summarizing major modules and structure**


**Source Layout**
- `src/transformers/__init__.py` exposes the public API; most higher-level modules (model loading, tokenizers, trainer, pipelines) live beside it so they can be imported directly.
- The repo keeps single-purpose utility modules at the top level: modeling helpers (`modeling_utils.py`, `modeling_layers.py`, `modeling_attn_mask_utils.py`, `modeling_flash_attention_utils.py`), tokenization helpers (`tokenization_utils_base.py`, `tokenization_utils_tokenizers.py`, `convert_slow_tokenizer.py`), multimodal preprocessors (`image_processing_utils.py`, `audio_utils.py`, `video_utils.py`, `time_series_utils.py`), and infra pieces like `cache_utils.py`, `dynamic_module_utils.py`, `dependency_versions_check.py`.
- Subpackages handle larger feature areas:
  - `src/transformers/models/` contains one subfolder per architecture (e.g., `bert`, `llama`, `qwen2`), each with config/model/tokenizer files plus conversion scripts.
  - `src/transformers/pipelines/` implements the task-specific pipeline classes and registry used by the high-level API.
  - `src/transformers/generation/` houses decoding strategies, beam search helpers, logits processors, and streaming utilities shared across models.
  - `src/transformers/trainer*.py` files plus `training_args*.py` define the PyTorch Trainer stack; `src/transformers/integrations/` adds callbacks for popular experiment frameworks (TensorBoard, Weights & Biases, Ray Tune, etc.).
  - `src/transformers/data/` provides dataset collators, processors, and tokenization datasets; `loss/` and `kernels/` collect custom loss functions and CUDA kernels like Flash Attention wrappers.
  - `src/transformers/distributed/` and `quantizers/` cover FSDP/DeepSpeed helpers and quantization flows; `cli/` implements command-line tools such as `transformers-cli`.
- Support folders `utils/` (logging, imports, testing), `integrations/`, and `pipelines/` interconnect the ecosystem, while `modelcard.py`, `model_debugging_utils.py`, and `safetensors_conversion.py` cover ancillary features like Hub metadata and format conversion.

Let me know if you want a deeper dive into any submodule (models, pipelines, trainer, etc.) or need to inspect specific files.