Instructions to use GuoxinChen/ReForm-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GuoxinChen/ReForm-32B with Transformers:

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

pipe = pipeline("text-generation", model="GuoxinChen/ReForm-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GuoxinChen/ReForm-32B")
model = AutoModelForCausalLM.from_pretrained("GuoxinChen/ReForm-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GuoxinChen/ReForm-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GuoxinChen/ReForm-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GuoxinChen/ReForm-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GuoxinChen/ReForm-32B

SGLang

How to use GuoxinChen/ReForm-32B 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 "GuoxinChen/ReForm-32B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GuoxinChen/ReForm-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "GuoxinChen/ReForm-32B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GuoxinChen/ReForm-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GuoxinChen/ReForm-32B with Docker Model Runner:
```
docker model run hf.co/GuoxinChen/ReForm-32B
```

Add pipeline tag and library name to model card

by nielsr HF Staff - opened Oct 31, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

nielsr

Oct 31, 2025

This PR improves the model card by adding the pipeline_tag: text-generation and library_name: transformers to its metadata.

The pipeline_tag ensures the model is discoverable under text generation tasks on the Hugging Face Hub.
The library_name enables the automated "how to use" widget, providing convenient code snippets for users, as the model explicitly uses transformers in its quick start guide.

All existing content, including links to the paper and code, remains unchanged.

Add pipeline tag and library name to model card1ca31ebc

GuoxinChen changed pull request status to merged Oct 31, 2025

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