Instructions to use teapotai/teapotllm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teapotai/teapotllm with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="teapotai/teapotllm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("teapotai/teapotllm")
model = AutoModelForSeq2SeqLM.from_pretrained("teapotai/teapotllm")

Transformers.js

How to use teapotai/teapotllm with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'teapotai/teapotllm');

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use teapotai/teapotllm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teapotai/teapotllm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teapotai/teapotllm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/teapotai/teapotllm

SGLang

How to use teapotai/teapotllm with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "teapotai/teapotllm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teapotai/teapotllm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "teapotai/teapotllm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teapotai/teapotllm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use teapotai/teapotllm with Docker Model Runner:
```
docker model run hf.co/teapotai/teapotllm
```

Serving Model via mlserver-huggingface

by gdagil - opened Apr 6, 2025

Discussion

gdagil

Apr 6, 2025

•

edited Apr 6, 2025

Hi,

I'm encountering an error while trying to serve your model using mlserver-huggingface.
Here’s the error message I received:

[mlserver] INFO - Couldn't load model 'teapotllm'. Model will be removed from registry.
[mlserver.parallel] ERROR - An error occurred processing a model update of type 'Load'.
Traceback (most recent call last):
  File "/opt/conda/lib/python3.10/site-packages/mlserver/registry.py", line 167, in _load_model
    model.ready = await model.load()
  File "/opt/conda/lib/python3.10/site-packages/mlserver_huggingface/runtime.py", line 29, in load
    self._model = load_pipeline_from_settings(self.hf_settings, self.settings)
  File "/opt/conda/lib/python3.10/site-packages/mlserver_huggingface/common.py", line 53, in load_pipeline_from_settings
    hf_pipeline = pipeline(
  File "/opt/conda/lib/python3.10/site-packages/transformers/pipelines/__init__.py", line 1047, in pipeline
    tokenizer = AutoTokenizer.from_pretrained(
  File "/opt/conda/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py", line 934, in from_pretrained
    return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2036, in from_pretrained
    return cls._from_pretrained(
  File "/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2074, in _from_pretrained
    slow_tokenizer = (cls.slow_tokenizer_class)._from_pretrained(
  File "/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2276, in _from_pretrained
    tokenizer = cls(*init_inputs, **init_kwargs)
  File "/opt/conda/lib/python3.10/site-packages/transformers/models/t5/tokenization_t5.py", line 150, in __init__
    self.sp_model.Load(vocab_file)
  File "/opt/conda/lib/python3.10/site-packages/sentencepiece/__init__.py", line 961, in Load
    return self.LoadFromFile(model_file)
  File "/opt/conda/lib/python3.10/site-packages/sentencepiece/__init__.py", line 316, in LoadFromFile
    return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
TypeError: not a string

It seems that the model fails to load due to an issue with the tokenizer.

My question is: How can I perform inference with this model without using the teapotai Python package?

Thank you for your assistance!

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