Hugging Face MCP服务器

Hugging Face MCP服务器

将AI助手连接到Hugging Face Hub。设置：https://huggingface.co/settings/mcp

使用场景和示例

找到适合任务的最佳模型

User: "Find the best model for code generation"

1. model_search(task="text-generation", query="code", sort="trendingScore", limit=10)
2. hub_repo_details(repo_ids=["top-result-id"], include_readme=true)

比较不同提供商的模型

User: "Compare Llama vs Qwen for text generation"

1. model_search(author="meta-llama", task="text-generation", sort="downloads", limit=5)
2. model_search(author="Qwen", task="text-generation", sort="downloads", limit=5)
3. hub_repo_details(repo_ids=["meta-llama/Llama-3.2-1B", "Qwen/Qwen3-8B"], include_readme=true)

查找训练数据集

User: "Find datasets for sentiment analysis in English"

1. dataset_search(query="sentiment", tags=["language:en", "task_categories:text-classification"], sort="downloads")
2. hub_repo_details(repo_ids=["top-dataset-id"], repo_type="dataset", include_readme=true)

发现AI工具（MCP Spaces）

User: "Find a tool that can remove image backgrounds"

1. space_search(query="background removal", mcp=true)
2. dynamic_space(operation="view_parameters", space_name="result-space-id")
3. dynamic_space(operation="invoke", space_name="result-space-id", parameters="{...}")

生成图像

User: "Create an image of a robot reading a book"

1. dynamic_space(operation="discover")  # 查看可用任务
2. gr1_flux1_schnell_infer(prompt="a robot sitting in a library reading a book, warm lighting, detailed")

研究某个主题

User: "What are the latest papers on RLHF?"

1. paper_search(query="reinforcement learning from human feedback", results_limit=10)
2. hub_repo_details(repo_ids=["paper-linked-model"], include_readme=true)  # 如果论文链接到模型

学习如何使用库

User: "How do I fine-tune with LoRA using PEFT?"

1. hf_doc_search(query="LoRA fine-tuning", product="peft")
2. hf_doc_fetch(doc_url="https://huggingface.co/docs/peft/...")

运行快速GPU作业

User: "Run this Python script on a GPU"

hf_jobs(operation="uv", args={
  "script": "# /// script\n# dependencies = [\"torch\"]\n# ///\nimport torch\nprint(torch.cuda.is_available())",
  "flavor": "t4-small"
})

在云GPU上训练模型

User: "Run my training script on an A10G"

hf_jobs(operation="run", args={
  "image": "pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime",
  "command": ["/bin/sh", "-lc", "pip install transformers trl && python train.py"],
  "flavor": "a10g-small",
  "secrets": {"HF_TOKEN": "$HF_TOKEN"}
})

检查作业状态

User: "What's happening with my training job?"

1. hf_jobs(operation="ps")
2. hf_jobs(operation="logs", args={"job_id": "job-xxxxx"})

探索当前趋势

User: "What models are trending right now?"

model_search(sort="trendingScore", limit=20)

获取模型卡片详情

User: "Tell me about Mistral-7B"

hub_repo_details(repo_ids=["mistralai/Mistral-7B-v0.1"], include_readme=true)

查找量化模型

User: "Find GGUF versions of Llama 3"

model_search(query="Llama 3 GGUF", sort="downloads", limit=10)

将Gradio Space用作工具

User: "Transcribe this audio file"

1. space_search(query="speech to text transcription", mcp=true)
2. dynamic_space(operation="view_parameters", space_name="openai/whisper")
3. dynamic_space(operation="invoke", space_name="openai/whisper", parameters="{\"audio\": \"...\"}")

安排定期作业

User: "Run this data sync every day at midnight"

hf_jobs(operation="scheduled uv", args={
  "script": "...",
  "cron": "0 0 * * *",
  "flavor": "cpu-basic"
})

工具选择指南

| 目标 | 工具 |

|------|------|

| 查找模型 | model_search |

| 查找数据集 | dataset_search |

| 查找Spaces/应用 | space_search |

| 查找论文 | paper_search |

| 获取仓库README/详情 | hub_repo_details |

| 学习库使用 | hf_doc_search → hf_doc_fetch |

| 在GPU/CPU上运行代码 | hf_jobs |

| 将Gradio应用用作工具 | dynamic_space |

| 生成图像 | gr1_flux1_schnell_infer 或 dynamic_space |

| 检查认证 | hf_whoami |

提示

• 使用 sort="trendingScore" 查找当前流行的内容
• 使用 sort="downloads" 查找经过验证的选项
• 在 space_search 中设置 mcp=true 以查找可用作工具的Spaces
• 在 hub_repo_details 中使用 include_readme=true 获取完整的模型/数据集文档
• 对于访问私有仓库的作业，始终包含 secrets: {"HF_TOKEN": "$HF_TOKEN"}
• 使用 dynamic_space(operation="discover") 查看所有可用的基于Space的任务