# Why am i getting this error while using huggingface api

**URL:** <https://discuss.huggingface.co/t/why-am-i-getting-this-error-while-using-huggingface-api/172159>\
**Category:** Beginners\
**Created:** [January 2, 2026, 3:08am UTC](https://discuss.huggingface.co/t/why-am-i-getting-this-error-while-using-huggingface-api/172159 "2026-01-02T03:08:10Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![sandsoftimes](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/sandsoftimes/32/56835_2.png) [@sandsoftimes](https://discuss.huggingface.co/u/sandsoftimes)\
**Post date:** [January 2, 2026, 3:08am UTC](https://discuss.huggingface.co/t/why-am-i-getting-this-error-while-using-huggingface-api/172159/1 "2026-01-02T03:08:10Z")

</div>

Until the model variable, I printed the model, and it shows that the model is successfully loaded when I execute the model.invoke() line. However, I’m getting this error. What is the reason for this error, i want to understand the cause of it

**Code:**

```auto
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint # (to use hugging face api)
from dotenv import load_dotenv 

load_dotenv()

llm = HuggingFaceEndpoint(
    repo_id="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    task ="text-generation",
)

model = ChatHuggingFace(llm=llm)

result = model.invoke("How are you?")

print(result.content)

```

**Error:**

```auto
lubyy@lubyy-virtualbox:~/langchain-models$ source /home/lubyy/langchain-models/langchain-models/bin/activate
(langchain-models) lubyy@lubyy-virtualbox:~/langchain-models$ python ./langchain-models/chatmodels/4_chatmodel_hf_api.py
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
Traceback (most recent call last):
  File "/home/lubyy/langchain-models/./langchain-models/chatmodels/4_chatmodel_hf_api.py", line 13, in <module>
    result = model.invoke("How are you?")
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lubyy/langchain-models/langchain-models/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 398, in invoke
    self.generate_prompt(
  File "/home/lubyy/langchain-models/langchain-models/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 1117, in generate_prompt
    return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lubyy/langchain-models/langchain-models/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 927, in generate
    self._generate_with_cache(
  File "/home/lubyy/langchain-models/langchain-models/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 1221, in _generate_with_cache
    result = self._generate(
             ^^^^^^^^^^^^^^^
  File "/home/lubyy/langchain-models/langchain-models/lib/python3.12/site-packages/langchain_huggingface/chat_models/huggingface.py", line 750, in _generate
    answer = self.llm.client.chat_completion(messages=message_dicts, **params)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lubyy/langchain-models/langchain-models/lib/python3.12/site-packages/huggingface_hub/inference/_client.py", line 878, in chat_completion
    provider_helper = get_provider_helper(
                      ^^^^^^^^^^^^^^^^^^^^
  File "/home/lubyy/langchain-models/langchain-models/lib/python3.12/site-packages/huggingface_hub/inference/_providers/ __init__.py", line 217, in get_provider_helper
    provider = next(iter(provider_mapping)).provider
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
StopIteration

```

---

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**Author:** ![system](https://us1.discourse-cdn.com/hellohellohello/original/2X/d/de4155eb4aa4108ecb32a1389d7cc37ae69f88b7.png) [@system](https://discuss.huggingface.co/u/system)\
**Post date:** [January 2, 2026, 3:08am UTC](https://discuss.huggingface.co/t/why-am-i-getting-this-error-while-using-huggingface-api/172159/2 "2026-01-02T03:08:11Z")

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---

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**Author:** ![system](https://us1.discourse-cdn.com/hellohellohello/original/2X/d/de4155eb4aa4108ecb32a1389d7cc37ae69f88b7.png) [@system](https://discuss.huggingface.co/u/system)\
**Post date:** [January 2, 2026, 9:53am UTC](https://discuss.huggingface.co/t/why-am-i-getting-this-error-while-using-huggingface-api/172159/3 "2026-01-02T09:53:54Z")

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---

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**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:** [January 2, 2026, 11:47pm UTC](https://discuss.huggingface.co/t/why-am-i-getting-this-error-while-using-huggingface-api/172159/4 "2026-01-02T23:47:52Z")

</div>

The transition from the old Inference API to Inference Providers has completely changed how it’s used from LangChain. For details, it’s faster to ask in the LangChain-related community.

* * *

You are getting `StopIteration` because **Hugging Face cannot find any inference “provider” to serve that model via the API route LangChain is calling** , so the provider list is empty and the library crashes when it tries to pick “the first provider”.

That is the actual cause in your stack trace.

* * *

## What “successfully loaded” means in your code

Up to this line:

```python
model = ChatHuggingFace(llm=llm)

```

you have **not loaded model weights** from Hugging Face into memory and you have **not made a real inference request** yet.

What _does_ happen at construction time:

- `ChatHuggingFace` resolves the model id and **loads the tokenizer/config from the Hub** (not the full model weights). LangChain’s reference docs describe this behavior. ([LangChain Docs](https://reference.langchain.com/python/integrations/langchain_huggingface/ "HuggingFace | LangChain Reference"))

So printing `model` only proves “wrapper object created” and “tokenizer/config may be downloadable”. It does not prove “remote inference is available”.

The first “real” call is:

```python
result = model.invoke("How are you?")

```

That’s where things break.

* * *

## What endpoint you are _actually_ calling

Your traceback includes:

```auto
answer = self.llm.client.chat_completion(...)
...
huggingface_hub/inference/_client.py ... chat_completion
...
get_provider_helper(...)
provider = next(iter(provider_mapping)).provider
StopIteration

```

So `ChatHuggingFace` is triggering Hugging Face’s **chat completion** path (`InferenceClient.chat_completion`), not a local `transformers` forward pass.

Background:

- Hugging Face “Inference Providers” is a router system: requests get routed to one of several providers (HF Inference, Together, Groq, etc.). ([Hugging Face](https://huggingface.co/docs/inference-providers/en/index "Inference Providers"))
- When `provider="auto"`, the client picks the _first available provider_ for that model (based on your provider order settings). ([Hugging Face](https://huggingface.co/docs/huggingface_hub/v0.31.0/en/guides/inference "Run Inference on servers"))
- If there are **zero** providers for that model, the internal mapping is empty and `next(iter(...))` raises `StopIteration`. That is exactly your crash.

* * *

## The specific reason it fails for TinyLlama

On the model page for:

`TinyLlama/TinyLlama-1.1B-Chat-v1.0`

the “Inference Providers” section explicitly says:

> **“This model isn’t deployed by any Inference Provider.”** ([Hugging Face](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 "TinyLlama/TinyLlama-1.1B-Chat-v1.0 · Hugging Face"))

That means the router has **nowhere** to send your chat completion request.

So:

- `provider_mapping = {}` (empty)
- `next(iter(provider_mapping))` throws
- you see `StopIteration`

This is also the same explanation given in similar community Q&A for this exact model + LangChain setup. ([Stack Overflow](https://stackoverflow.com/questions/79616351/facing-issue-using-a-model-hosted-on-huggingface-server-and-talking-to-it-using "Facing issue using a model hosted on HuggingFace ..."))

* * *

## What about the “None of PyTorch / TensorFlow / Flax found” warning?

That warning comes from `transformers` detecting you do not have a deep learning backend installed.

- It matters if you run the model **locally** (because then you need PyTorch or similar).
- It does **not** directly cause your `StopIteration`, which happens in the Hugging Face inference routing code before any local model execution.

So treat it as a separate issue: relevant only if you switch to local inference.

* * *

# Solutions (choose based on what you want)

## Solution 1: Use a model that is actually deployed by an Inference Provider (serverless API)

This is the simplest if your goal is “call Hugging Face like an API”.

What to do:

1. Pick a model whose model page shows it _is_ available via Inference Providers (unlike TinyLlama). ([Hugging Face](https://huggingface.co/docs/hub/en/models-inference "Inference Providers"))
2. In LangChain, set `provider="auto"` (LangChain docs show this). ([LangChain Document](https://docs.langchain.com/oss/python/integrations/chat/huggingface "ChatHuggingFace - Docs by LangChain"))
3. Ensure your token is permitted for Inference Providers calls (HF docs require a token with “Inference Providers” permission). ([Hugging Face](https://huggingface.co/docs/inference-providers/en/tasks/chat-completion "Chat Completion"))

Example pattern (model id is just an example; use any provider-deployed model):

```python
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint

llm = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1-0528",
    task="text-generation",
    provider="auto",
    max_new_tokens=128,
)

chat = ChatHuggingFace(llm=llm)
print(chat.invoke("How are you?").content)

```

The key change is: **use a provider-backed model** and **enable provider selection**. ([LangChain Document](https://docs.langchain.com/oss/python/integrations/chat/huggingface "ChatHuggingFace - Docs by LangChain"))

* * *

## Solution 2: Keep TinyLlama, but deploy it (Inference Endpoints or your own server)

Because TinyLlama is not deployed by any provider, the router cannot serve it. ([Hugging Face](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 "TinyLlama/TinyLlama-1.1B-Chat-v1.0 · Hugging Face"))

If you must use TinyLlama remotely:

- Deploy it via Hugging Face **Inference Endpoints** (dedicated managed infra), or
- Serve it yourself (TGI, vLLM, etc.) and point your client at that server.

Hugging Face’s inference guide explains that `InferenceClient` can target dedicated endpoints and providers, and that the new client replaces the legacy API style. ([Hugging Face](https://huggingface.co/docs/huggingface_hub/v0.31.0/en/guides/inference "Run Inference on servers"))

* * *

## Solution 3: Run TinyLlama locally (matches the model card)

TinyLlama’s model card “How to use” section is a local `transformers` pipeline example. ([Hugging Face](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 "TinyLlama/TinyLlama-1.1B-Chat-v1.0 · Hugging Face"))

If you go local:

- Install PyTorch (that warning goes away).
- Use `transformers` directly or a LangChain local pipeline wrapper.

This is the best path if your goal is “use this specific tiny model cheaply” and you do not need hosted inference.

* * *

# Two common pitfalls to avoid

## Pitfall A: Token env var mismatch

LangChain docs talk about `HUGGINGFACEHUB_API_TOKEN`. ([LangChain Document](https://docs.langchain.com/oss/python/integrations/chat/huggingface "ChatHuggingFace - Docs by LangChain"))  
Hugging Face Inference Providers docs commonly refer to `HF_TOKEN` and require the token permission for Inference Providers. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/tasks/chat-completion "Chat Completion"))

Practical tip: set both env vars to the same token while debugging.

## Pitfall B: “task=text-generation” does not force chat wrappers to use text-generation

You used a **chat** wrapper (`ChatHuggingFace`), so it called `chat_completion` anyway (as your traceback shows). If you want pure text-generation semantics, don’t wrap it as a chat model.

* * *

## Quick debug checklist for your case

1. Open the model page and look at “Inference Providers”.

2. If using serverless inference, pick a model that is deployed by a provider and set `provider="auto"`. ([LangChain Document](https://docs.langchain.com/oss/python/integrations/chat/huggingface "ChatHuggingFace - Docs by LangChain"))

3. Ensure your HF token has “Inference Providers” permission. ([Hugging Face](https://huggingface.co/docs/inference-providers/en/tasks/chat-completion "Chat Completion"))

4. Ignore the PyTorch warning unless you choose local inference.

* * *

### Summary

- You did not load the model. You created wrappers and downloaded tokenizer/config. ([LangChain Docs](https://reference.langchain.com/python/integrations/langchain_huggingface/ "HuggingFace | LangChain Reference"))
- `invoke()` calls Hugging Face **chat completion** routing, which needs at least one deployed provider. ([Hugging Face](https://huggingface.co/docs/huggingface_hub/v0.31.0/en/guides/inference "Run Inference on servers"))
- TinyLlama explicitly has **no Inference Provider deployment** , so routing returns an empty provider list and you get `StopIteration`. ([Hugging Face](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 "TinyLlama/TinyLlama-1.1B-Chat-v1.0 · Hugging Face"))
- Fix: switch to a provider-backed model, or deploy TinyLlama yourself, or run it locally.