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  "path": "/t/error-while-using-langchain-with-huggingface-models/174977#post_2",
  "publishedAt": "2026-04-05T07:52:46.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "GitHub",
    "Hugging Face",
    "7x.mintlify.app",
    "LangChain",
    "LangChain Documentation"
  ],
  "textContent": "Because the method for using Hugging Face Inference with LangChain has changed frequently in the past, information online is often confusing, making it difficult to find the correct solution through search engines or AI…\n\n* * *\n\nThe most likely cause in your case is **not** the prompt template and **not** the chain operator. It is usually this: `HuggingFaceEndpoint` is making a **text-generation** request, while `mistralai/Mistral-7B-Instruct-v0.3` is often exposed by the current Hugging Face provider routing as a **chat / conversational** model instead. There is a public LangChain issue with this exact model and this exact error family, and the `HuggingFaceEndpoint` reference explicitly says the class is for models that support the **text generation** task. (GitHub)\n\n## Why your code looks correct but still fails\n\nYour code structure is valid:\n\n\n    prompt = PromptTemplate(...)\n    llm = HuggingFaceEndpoint(...)\n    chain = prompt | llm\n    response = chain.invoke(...)\n\n\nThat pattern is fine. The failure usually happens one layer lower, when LangChain calls Hugging Face through `HuggingFaceEndpoint`. In the reported Mistral case, the stack trace shows `HuggingFaceEndpoint` calling `client.text_generation(...)`, and the backend rejects it because that model is mapped to `conversational` for that provider instead of `text-generation`. (GitHub)\n\n## The main background\n\nHugging Face now separates **Text Generation** and **Chat Completion** more clearly than many older examples did. The Text Generation docs describe prompt-based generation, while the Chat Completion docs describe message-based conversational generation. On top of that, Hugging Face Inference Providers automatically route requests to a provider, and support depends on the **model + provider + task** combination, not just the model name alone. (Hugging Face)\n\nThat is why this can fail even though the code looks simple. The prompt is valid. The chain is valid. But the selected backend may say: “this model is available here as chat, not as plain text generation.” (GitHub)\n\n* * *\n\n## Causes, ranked for your exact code\n\n### 1. Most likely: model-task mismatch\n\nThis is the best match for your case.\n\n`HuggingFaceEndpoint` is for **text-generation** models. Your chosen model is an **instruct/chat-style** model, and there is a documented case where this exact model fails with:\n\n\n    ValueError: Model mistralai/Mistral-7B-Instruct-v0.3 is not supported for task text-generation ...\n    Supported task: conversational\n\n\nThat means the wrapper is asking for one kind of inference, while the provider only offers another kind for that model. (GitHub)\n\n### 2. Also likely: you are using the older import path\n\nYour code imports:\n\n\n    from langchain_community.llms import HuggingFaceEndpoint\n\n\nCurrent LangChain docs use the dedicated Hugging Face integration package:\n\n\n    from langchain_huggingface import HuggingFaceEndpoint\n\n\nand the current LangChain docs and chat docs both point to `langchain-huggingface` as the active integration path. Using the older community path can leave you on examples or behavior that do not match the current Hugging Face routing model. (7x.mintlify.app)\n\n### 3. Sometimes relevant: missing explicit `task=\"text-generation\"`\n\nThere is a separate LangChain issue where `HuggingFaceEndpoint` raised a `ValueError` until `task=\"text-generation\"` was added explicitly. That is a real issue, but it is a different one. If your actual error says the model only supports `conversational`, then adding `task=\"text-generation\"` alone will not solve the real mismatch. It only fixes the “missing task” variant. (GitHub)\n\n### 4. Worth checking: token and provider setup\n\nHugging Face’s current Inference Providers docs say you should use a fine-grained token with **Make calls to Inference Providers** permission. They also note that provider selection is automatic by default, unless you pin one yourself. Wrong token permissions or provider availability problems can produce other failures, although those often show up as authorization or provider-selection errors rather than your exact `ValueError`. (Hugging Face)\n\n* * *\n\n## What your code is really doing\n\nWhen you write this:\n\n\n    llm = HuggingFaceEndpoint(\n        repo_id=\"mistralai/Mistral-7B-Instruct-v0.3\",\n        temperature=0.7,\n        timeout=300\n    )\n\n\nyou are saying, in effect:\n\n> “Treat this model like a plain text-generation endpoint.”\n\nThat is the key assumption. For many models that is fine. For this exact Mistral model on current provider-backed inference, that assumption often breaks. The public issue for this model shows the failure is coming from the text-generation path, not from `PromptTemplate` or LCEL chaining. (GitHub)\n\n* * *\n\n## The best fixes\n\n## Fix 1. Move to the current package first\n\nInstall the current packages:\n\n\n    pip install -U langchain langchain-huggingface huggingface_hub\n\n\nThen import from the current package:\n\n\n    from langchain_huggingface import HuggingFaceEndpoint\n\n\nThat aligns your code with the current documented integration path. LangChain’s current Hugging Face docs use `langchain_huggingface`, not `langchain_community`, for these examples. (7x.mintlify.app)\n\n* * *\n\n## Fix 2. If you want plain prompt → text output, use a model that supports text-generation\n\n`HuggingFaceEndpoint` is the right tool only when the model supports **text generation** on the chosen backend. The reference page says exactly that. Hugging Face’s text-generation docs also treat this as a separate task from chat completion. (LangChain)\n\nA safer version of your code looks like this:\n\n\n    from langchain_core.prompts import PromptTemplate\n    from langchain_huggingface import HuggingFaceEndpoint\n    import os\n\n    os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = \"hf_your_token_here\"\n\n    prompt = PromptTemplate(\n        input_variables=[\"product\"],\n        template=\"What is a good name for a company that makes {product}?\"\n    )\n\n    llm = HuggingFaceEndpoint(\n        repo_id=\"Qwen/Qwen2.5-7B-Instruct-1M\",\n        task=\"text-generation\",\n        provider=\"auto\",\n        max_new_tokens=64,\n        temperature=0.7,\n        timeout=300,\n    )\n\n    chain = prompt | llm\n    response = chain.invoke({\"product\": \"camera\"})\n    print(\"AI Suggestion:\", response)\n\n\nThis fix does two things:\n\n  * it uses the current package path,\n  * it makes the task explicit for a model path that is documented in current Hugging Face task docs. (LangChain Documentation)\n\n\n\n* * *\n\n## Fix 3. If you want to keep `mistralai/Mistral-7B-Instruct-v0.3`, treat it as a chat model\n\nThis is the more natural choice for an instruct model. LangChain’s chat docs show `ChatHuggingFace` instantiated from a `HuggingFaceEndpoint`, and Hugging Face’s own docs define chat completion as the message-based interface for conversational models. (LangChain Documentation)\n\nExample:\n\n\n    from langchain_core.prompts import ChatPromptTemplate\n    from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace\n    import os\n\n    os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = \"hf_your_token_here\"\n\n    prompt = ChatPromptTemplate.from_messages([\n        (\"human\", \"What is a good name for a company that makes {product}?\")\n    ])\n\n    llm = HuggingFaceEndpoint(\n        repo_id=\"mistralai/Mistral-7B-Instruct-v0.3\",\n        task=\"text-generation\",\n        provider=\"auto\",\n        max_new_tokens=64,\n        temperature=0.7,\n        timeout=300,\n    )\n\n    chat_model = ChatHuggingFace(llm=llm)\n\n    chain = prompt | chat_model\n    response = chain.invoke({\"product\": \"camera\"})\n    print(\"AI Suggestion:\", response.content)\n\n\nImportant nuance: even here, `HuggingFaceEndpoint` still sits underneath. This approach is most useful when LangChain’s chat wrapper and the selected provider/model route agree. For provider-backed inference, a direct chat client can sometimes be even clearer. (LangChain Documentation)\n\n* * *\n\n## Fix 4. Test the model outside LangChain first\n\nThis is the fastest way to separate:\n\n  * “LangChain wrapper problem”\nfrom\n  * “model/provider/task problem.”\n\n\n\nHugging Face’s Inference Providers docs show `InferenceClient` using chat completions directly for conversational models. (Hugging Face)\n\nExample:\n\n\n    from huggingface_hub import InferenceClient\n\n    client = InferenceClient()\n\n    response = client.chat.completions.create(\n        model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n        messages=[\n            {\"role\": \"user\", \"content\": \"What is a good name for a company that makes camera products?\"}\n        ],\n        max_tokens=64,\n    )\n\n    print(response.choices[0].message.content)\n\n\nIf this works and your LangChain code does not, then the problem is almost certainly the wrapper choice rather than the model itself. Hugging Face documents `chat.completions.create` as the chat-completion path, and the source reference shows it is the intended method for message-based chat use. (Hugging Face)\n\n* * *\n\n## Fix 5. If your real error is the “missing task” version, add `task`\n\nThis is the smaller fix.\n\n\n    llm = HuggingFaceEndpoint(\n        repo_id=\"some-text-generation-model\",\n        task=\"text-generation\",\n        temperature=0.7,\n        timeout=300,\n    )\n\n\nThat exact change resolved a documented issue where the docs omitted the needed task parameter. (GitHub)\n\n* * *\n\n## A practical debug checklist\n\nUse this order.\n\n  1. Upgrade to the current package path:\n\n     * `langchain-huggingface`\n     * `huggingface_hub`\n(7x.mintlify.app)\n  2. Make sure your token is a fine-grained token with **Make calls to Inference Providers** permission.\n(Hugging Face)\n\n  3. If you stay with `HuggingFaceEndpoint`, add `task=\"text-generation\"`.\n(GitHub)\n\n  4. If the error says `Supported task: conversational`, stop trying to force that model through a plain text-generation flow. Switch to a chat-model path or choose a model/provider combination documented for text generation.\n(GitHub)\n\n  5. Test the same model once with `InferenceClient.chat.completions.create(...)`. That tells you whether the model route itself works.\n(Hugging Face)\n\n\n\n\n* * *\n\n## My judgment for your exact snippet\n\nHere is the probability ranking I would use.\n\n  * **Most likely:** `HuggingFaceEndpoint` is calling the text-generation path, but your chosen Mistral model is being exposed by the provider as conversational instead. (GitHub)\n  * **Next most likely:** you are mixing current Hugging Face routing with the older `langchain_community` integration path. (7x.mintlify.app)\n  * **Possible:** you also need an explicit `task=\"text-generation\"` because of your installed versions. (GitHub)\n  * **Possible but less likely for this exact error:** token/provider configuration issue. (Hugging Face)\n\n\n\nSo the cleanest summary is:\n\n> Your prompt and chain are probably fine. The main problem is that you are using a **text-generation wrapper** with a model that the current Hugging Face backend often exposes as **chat/conversational** instead. (GitHub)",
  "title": "Error While using langchain with huggingface models"
}