# Error While using langchain with huggingface models

**URL:** <https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977>\
**Category:** Models\
**Created:** [April 5, 2026, 6:52am UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977 "2026-04-05T06:52:16Z")\
**Posts on this page:** 10\
**Page:** 1

<div class="post-metadata">

**Author:** ![hazrd](https://avatars.discourse-cdn.com/v4/letter/h/258eb7/32.png) [@hazrd](https://discuss.huggingface.co/u/hazrd)\
**Post date:** [April 5, 2026, 6:52am UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/1 "2026-04-05T06:52:16Z")

</div>

from langchain\_core.prompts import PromptTemplate  
from langchain\_community.llms import HuggingFaceEndpoint  
import os

os.environ[“HUGGINGFACEHUB\_API\_TOKEN”] = “hf\_your\_new\_token\_here”

prompt = PromptTemplate(  
input\_variables=[“product”],  
template=“What is a good name for a company that makes {product}?”  
)

llm = HuggingFaceEndpoint(  
repo\_id=“mistralai/Mistral-7B-Instruct-v0.3”,  
temperature=0.7,  
timeout=300

)

chains = prompt | llm  
print(“LLM Initialized with Token!”)

try:  
response = chains.invoke({“product”: “camera”})  
print(“AI Suggestion:”, response)  
except Exception as e:  
print(f"Error details: {e}")

when i run this i get Value error can anyone help me out? Its a basic prompt template and text gen code but still it doesnt work i used various models from Huggingface and its not working well with langchain while chaining the llm with prompt and invoking it.

---

<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [April 5, 2026, 7:52am UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/2 "2026-04-05T07:52:46Z")

</div>

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…

* * *

The 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](https://github.com/langchain-ai/langchain/issues/31434 "HuggingFaceEndpoint fails bug · Issue #31434 · langchain-ai/langchain · GitHub"))

## Why your code looks correct but still fails

Your code structure is valid:

```python
prompt = PromptTemplate(...)
llm = HuggingFaceEndpoint(...)
chain = prompt | llm
response = chain.invoke(...)

```

That 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](https://github.com/langchain-ai/langchain/issues/31434 "HuggingFaceEndpoint fails bug · Issue #31434 · langchain-ai/langchain · GitHub"))

## The main background

Hugging 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](https://huggingface.co/docs/inference-providers/tasks/text-generation "Text Generation · Hugging Face"))

That 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](https://github.com/langchain-ai/langchain/issues/31434 "HuggingFaceEndpoint fails bug · Issue #31434 · langchain-ai/langchain · GitHub"))

* * *

## Causes, ranked for your exact code

### 1. Most likely: model-task mismatch

This is the best match for your case.

`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:

```python
ValueError: Model mistralai/Mistral-7B-Instruct-v0.3 is not supported for task text-generation ...
Supported task: conversational

```

That means the wrapper is asking for one kind of inference, while the provider only offers another kind for that model. ([GitHub](https://github.com/langchain-ai/langchain/issues/31434 "HuggingFaceEndpoint fails bug · Issue #31434 · langchain-ai/langchain · GitHub"))

### 2. Also likely: you are using the older import path

Your code imports:

```python
from langchain_community.llms import HuggingFaceEndpoint

```

Current LangChain docs use the dedicated Hugging Face integration package:

```python
from langchain_huggingface import HuggingFaceEndpoint

```

and 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](https://7x.mintlify.app/oss/python/integrations/llms/huggingface_endpoint "Huggingface Endpoints - Docs by LangChain"))

### 3. Sometimes relevant: missing explicit `task="text-generation"`

There 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](https://github.com/langchain-ai/langchain/issues/29685 "DOC: HuggingFaceEndpoint Requires Explicit `task` Argument, but Documentation Omits It · Issue #29685 · langchain-ai/langchain · GitHub"))

### 4. Worth checking: token and provider setup

Hugging 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](https://huggingface.co/docs/inference-providers/en/index "Inference Providers · Hugging Face"))

* * *

## What your code is really doing

When you write this:

```python
llm = HuggingFaceEndpoint(
    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
    temperature=0.7,
    timeout=300
)

```

you are saying, in effect:

> “Treat this model like a plain text-generation endpoint.”

That 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](https://github.com/langchain-ai/langchain/issues/31434 "HuggingFaceEndpoint fails bug · Issue #31434 · langchain-ai/langchain · GitHub"))

* * *

## The best fixes

## Fix 1. Move to the current package first

Install the current packages:

```bash
pip install -U langchain langchain-huggingface huggingface_hub

```

Then import from the current package:

```python
from langchain_huggingface import HuggingFaceEndpoint

```

That 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](https://7x.mintlify.app/oss/python/integrations/llms/huggingface_endpoint "Huggingface Endpoints - Docs by LangChain"))

* * *

## Fix 2. If you want plain prompt → text output, use a model that supports text-generation

`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](https://reference.langchain.com/python/langchain-huggingface/llms/huggingface_endpoint/HuggingFaceEndpoint "HuggingFaceEndpoint | langchain\_huggingface"))

A safer version of your code looks like this:

```python
from langchain_core.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEndpoint
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_your_token_here"

prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?"
)

llm = HuggingFaceEndpoint(
    repo_id="Qwen/Qwen2.5-7B-Instruct-1M",
    task="text-generation",
    provider="auto",
    max_new_tokens=64,
    temperature=0.7,
    timeout=300,
)

chain = prompt | llm
response = chain.invoke({"product": "camera"})
print("AI Suggestion:", response)

```

This fix does two things:

- it uses the current package path,
- it makes the task explicit for a model path that is documented in current Hugging Face task docs. ([LangChain Documentation](https://docs.langchain.com/oss/python/integrations/chat/huggingface "ChatHuggingFace integration - Docs by LangChain"))

* * *

## Fix 3. If you want to keep `mistralai/Mistral-7B-Instruct-v0.3`, treat it as a chat model

This 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](https://docs.langchain.com/oss/python/integrations/chat/huggingface "ChatHuggingFace integration - Docs by LangChain"))

Example:

```python
from langchain_core.prompts import ChatPromptTemplate
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_your_token_here"

prompt = ChatPromptTemplate.from_messages([
    ("human", "What is a good name for a company that makes {product}?")
])

llm = HuggingFaceEndpoint(
    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
    task="text-generation",
    provider="auto",
    max_new_tokens=64,
    temperature=0.7,
    timeout=300,
)

chat_model = ChatHuggingFace(llm=llm)

chain = prompt | chat_model
response = chain.invoke({"product": "camera"})
print("AI Suggestion:", response.content)

```

Important 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](https://docs.langchain.com/oss/python/integrations/chat/huggingface "ChatHuggingFace integration - Docs by LangChain"))

* * *

## Fix 4. Test the model outside LangChain first

This is the fastest way to separate:

- “LangChain wrapper problem”  
from
- “model/provider/task problem.”

Hugging Face’s Inference Providers docs show `InferenceClient` using chat completions directly for conversational models. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/index "Inference Providers · Hugging Face"))

Example:

```python
from huggingface_hub import InferenceClient

client = InferenceClient()

response = client.chat.completions.create(
    model="mistralai/Mistral-7B-Instruct-v0.3",
    messages=[
        {"role": "user", "content": "What is a good name for a company that makes camera products?"}
    ],
    max_tokens=64,
)

print(response.choices[0].message.content)

```

If 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](https://huggingface.co/docs/inference-providers/en/index "Inference Providers · Hugging Face"))

* * *

## Fix 5. If your real error is the “missing task” version, add `task`

This is the smaller fix.

```python
llm = HuggingFaceEndpoint(
    repo_id="some-text-generation-model",
    task="text-generation",
    temperature=0.7,
    timeout=300,
)

```

That exact change resolved a documented issue where the docs omitted the needed task parameter. ([GitHub](https://github.com/langchain-ai/langchain/issues/29685 "DOC: HuggingFaceEndpoint Requires Explicit `task` Argument, but Documentation Omits It · Issue #29685 · langchain-ai/langchain · GitHub"))

* * *

## A practical debug checklist

Use this order.

1. Upgrade to the current package path:

2. Make sure your token is a fine-grained token with **Make calls to Inference Providers** permission.  
([Hugging Face](https://huggingface.co/docs/inference-providers/en/index "Inference Providers · Hugging Face"))

3. If you stay with `HuggingFaceEndpoint`, add `task="text-generation"`.  
([GitHub](https://github.com/langchain-ai/langchain/issues/29685 "DOC: HuggingFaceEndpoint Requires Explicit `task` Argument, but Documentation Omits It · Issue #29685 · langchain-ai/langchain · GitHub"))

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.  
([GitHub](https://github.com/langchain-ai/langchain/issues/31434 "HuggingFaceEndpoint fails bug · Issue #31434 · langchain-ai/langchain · GitHub"))

5. Test the same model once with `InferenceClient.chat.completions.create(...)`. That tells you whether the model route itself works.  
([Hugging Face](https://huggingface.co/docs/inference-providers/en/index "Inference Providers · Hugging Face"))

* * *

## My judgment for your exact snippet

Here is the probability ranking I would use.

- **Most likely:** `HuggingFaceEndpoint` is calling the text-generation path, but your chosen Mistral model is being exposed by the provider as conversational instead. ([GitHub](https://github.com/langchain-ai/langchain/issues/31434 "HuggingFaceEndpoint fails bug · Issue #31434 · langchain-ai/langchain · GitHub"))
- **Next most likely:** you are mixing current Hugging Face routing with the older `langchain_community` integration path. ([7x.mintlify.app](https://7x.mintlify.app/oss/python/integrations/llms/huggingface_endpoint "Huggingface Endpoints - Docs by LangChain"))
- **Possible:** you also need an explicit `task="text-generation"` because of your installed versions. ([GitHub](https://github.com/langchain-ai/langchain/issues/29685 "DOC: HuggingFaceEndpoint Requires Explicit `task` Argument, but Documentation Omits It · Issue #29685 · langchain-ai/langchain · GitHub"))
- **Possible but less likely for this exact error:** token/provider configuration issue. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/index "Inference Providers · Hugging Face"))

So the cleanest summary is:

> 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](https://github.com/langchain-ai/langchain/issues/31434 "HuggingFaceEndpoint fails bug · Issue #31434 · langchain-ai/langchain · GitHub"))

---

<div class="post-metadata">

**Author:** ![hazrd](https://avatars.discourse-cdn.com/v4/letter/h/258eb7/32.png) [@hazrd](https://discuss.huggingface.co/u/hazrd)\
**Post date:** [April 5, 2026, 8:28am UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/3 "2026-04-05T08:28:04Z")

</div>

I tried what you are trying to make me understand here, but i still got the same errors i got multiple errors, like  
ValueError, ValidationError, Value error, stop Iteration error too  
Possibly tried doing everything, could you please try and run it in an env and let me know if it works well?

---

<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [April 5, 2026, 9:28pm UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/4 "2026-04-05T21:28:48Z")

</div>

For `mistralai/Mistral-7B-Instruct-v0.3`, it doesn’t seem to be deployed (and therefore unusable) in the first place, so have you tried it with [models that is available](https://huggingface.co/models?inference_provider=all&sort=trending)?  
[https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)

 ![{B92AC0F6-15EB-40C1-8CCA-E7A5F2AB1ED8}](https://us1.discourse-cdn.com/hellohellohello/original/3X/2/c/2c961d3a580d644bcf1b96109118c757fe27e27a.png)

---

<div class="post-metadata">

**Author:** ![hazrd](https://avatars.discourse-cdn.com/v4/letter/h/258eb7/32.png) [@hazrd](https://discuss.huggingface.co/u/hazrd)\
**Post date:** [April 6, 2026, 12:37am UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/5 "2026-04-06T00:37:25Z")

</div>

I did not get what you’re trying to say, it isn’t deployed? Actually I’m new to huggingface hun and this Gen AI models and API but I’m trying to learn, can you explain it in a better way please?

Thank you.

---

<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [April 6, 2026, 2:17am UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/6 "2026-04-06T02:17:38Z")

</div>

> it isn’t deployed

Yes. Now, [few(?) models are deployed](https://huggingface.co/models?library=transformers&inference_provider=groq,novita,cerebras,sambanova,nscale,fal-ai,hyperbolic,together,fireworks-ai,featherless-ai,zai-org,replicate,cohere,scaleway,publicai,ovhcloud,hf-inference,wavespeed&sort=trending). There is virtually no way to use a model via API that hasn’t been deployed.  
Also, since the number of free API calls is quite limited, it’s best to think of this as a test purpose.

 ![langchainapi](https://us1.discourse-cdn.com/hellohellohello/original/3X/5/b/5b546dda03c481731449cb9f1ab65746795b86c6.jpeg)

If you plan to use it heavily, you should consider using a local LLM…

---

<div class="post-metadata">

**Author:** ![hazrd](https://avatars.discourse-cdn.com/v4/letter/h/258eb7/32.png) [@hazrd](https://discuss.huggingface.co/u/hazrd)\
**Post date:** [April 6, 2026, 3:05am UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/7 "2026-04-06T03:05:00Z")

</div>

Firstly thank you so much for the info.

So I will have to check for models with the ⚡ Inference? So those are models which can be used?

---

<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [April 6, 2026, 3:07am UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/8 "2026-04-06T03:07:01Z")

</div>

> have to check for models with the ⚡ Inference? So those are models which can be used?

Yes. Yes. However, since the free quota is limited to a few times a month, be careful when making API calls…😅

---

<div class="post-metadata">

**Author:** ![hazrd](https://avatars.discourse-cdn.com/v4/letter/h/258eb7/32.png) [@hazrd](https://discuss.huggingface.co/u/hazrd)\
**Post date:** [April 6, 2026, 1:08pm UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/9 "2026-04-06T13:08:48Z")

</div>

What do you mean by free quota? Is hugging face also paid? I just wanted to make small applications and test projects nothing big as such. I found issue with the Huggingface so i started using Groq API and their llama model. Its pretty good and the daily creds are enough for my project.

---

<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [April 6, 2026, 11:15pm UTC](https://discuss.huggingface.co/t/error-while-using-langchain-with-huggingface-models/174977/10 "2026-04-06T23:15:17Z")

</div>

> What do you mean by free quota? Is hugging face also paid?

Yeah. That’s right. Some features are free, while others require a paid subscription/PAYG. Take a look at this—well, when it comes to Inference, it’s definitely more on the paid side.

> **[Pricing and Billing · Hugging Face](https://huggingface.co/docs/inference-providers/pricing)**
>
> We’re on a journey to advance and democratize artificial intelligence through open source and open science.