Instructions to use launch/ThinkPRM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use launch/ThinkPRM-7B with Transformers:

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

pipe = pipeline("text-generation", model="launch/ThinkPRM-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("launch/ThinkPRM-7B")
model = AutoModelForCausalLM.from_pretrained("launch/ThinkPRM-7B")
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 launch/ThinkPRM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "launch/ThinkPRM-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "launch/ThinkPRM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/launch/ThinkPRM-7B

SGLang

How to use launch/ThinkPRM-7B 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 "launch/ThinkPRM-7B" \
    --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": "launch/ThinkPRM-7B",
		"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 "launch/ThinkPRM-7B" \
        --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": "launch/ThinkPRM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use launch/ThinkPRM-7B with Docker Model Runner:
```
docker model run hf.co/launch/ThinkPRM-7B
```

Add project page link to model card

by nielsr HF Staff - opened Sep 26, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

nielsr

Sep 26, 2025

This PR enhances the model card by adding a direct link to the project page (https://mukhal.github.io/projects/thinkprm/) under the "Model Sources" section. This makes it easier for users to find additional information and resources related to the ThinkPRM project.

The existing metadata, paper link, code link, and sample usage are already well-documented and validated by the provided information.

Add project page link to model card69d5cf8f

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