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:
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)
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:
result = model.invoke("How are you?")
That’s where things break.
What endpoint you are actually calling
Your traceback includes:
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)
- When
provider="auto", the client picks the first available provider for that model (based on your provider order settings). (Hugging Face)
- 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)
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)
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:
- Pick a model whose model page shows it is available via Inference Providers (unlike TinyLlama). (Hugging Face)
- In LangChain, set
provider="auto" (LangChain docs show this). (LangChain Document)
- Ensure your token is permitted for Inference Providers calls (HF docs require a token with “Inference Providers” permission). (Hugging Face)
Example pattern (model id is just an example; use any provider-deployed model):
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)
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)
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)
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)
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)
Hugging Face Inference Providers docs commonly refer to HF_TOKEN and require the token permission for Inference Providers. (Hugging Face)
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
-
Open the model page and look at “Inference Providers”.
- If it says “isn’t deployed”, serverless routing will fail. (Hugging Face)
-
If using serverless inference, pick a model that is deployed by a provider and set provider="auto". (LangChain Document)
-
Ensure your HF token has “Inference Providers” permission. (Hugging Face)
-
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)
invoke() calls Hugging Face chat completion routing, which needs at least one deployed provider. (Hugging Face)- TinyLlama explicitly has no Inference Provider deployment, so routing returns an empty provider list and you get
StopIteration. (Hugging Face)
- Fix: switch to a provider-backed model, or deploy TinyLlama yourself, or run it locally.