Instructions to use LangAGI-Lab/CoffeePots-Critic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LangAGI-Lab/CoffeePots-Critic with Transformers:

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

pipe = pipeline("text-generation", model="LangAGI-Lab/CoffeePots-Critic")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LangAGI-Lab/CoffeePots-Critic")
model = AutoModelForCausalLM.from_pretrained("LangAGI-Lab/CoffeePots-Critic", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LangAGI-Lab/CoffeePots-Critic with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LangAGI-Lab/CoffeePots-Critic

SGLang

How to use LangAGI-Lab/CoffeePots-Critic 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 "LangAGI-Lab/CoffeePots-Critic" \
    --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": "LangAGI-Lab/CoffeePots-Critic",
		"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 "LangAGI-Lab/CoffeePots-Critic" \
        --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": "LangAGI-Lab/CoffeePots-Critic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LangAGI-Lab/CoffeePots-Critic with Docker Model Runner:
```
docker model run hf.co/LangAGI-Lab/CoffeePots-Critic
```

CoffeePots-Critic

Commit History

Mar 2, 2025

23:08
Adding `safetensors` variant of this model⁠

SFconvertbot

Verified signature
8511ddd

Apr 16, 2024

20:39
Update config.json

hyungjoochae

Verified signature
1727ba8

Dec 23, 2023

19:02
Upload model

Lune-Blue

4010884
18:23
Upload tokenizer

Lune-Blue

6c69c70
18:22
initial commit

Lune-Blue

8f6535c

Commit History

Mar 2, 2025

Adding `safetensors` variant of this model⁠

Apr 16, 2024

Update config.json

Dec 23, 2023

Upload model

Upload tokenizer

initial commit