Instructions to use launch/ThinkPRM-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use launch/ThinkPRM-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="launch/ThinkPRM-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("launch/ThinkPRM-1.5B")
model = AutoModelForCausalLM.from_pretrained("launch/ThinkPRM-1.5B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use launch/ThinkPRM-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "launch/ThinkPRM-1.5B"
# 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-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/launch/ThinkPRM-1.5B

SGLang

How to use launch/ThinkPRM-1.5B 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-1.5B" \
    --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-1.5B",
		"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-1.5B" \
        --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-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use launch/ThinkPRM-1.5B with Docker Model Runner:
```
docker model run hf.co/launch/ThinkPRM-1.5B
```

mkhalifa commited on Jun 25, 2025

Commit

fcf2c6f

verified ·

1 Parent(s): 021087f

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -60,7 +60,7 @@ The model has been evaluated on mathematical reasoning (MATH, AIME), scientific
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from vllm import LLM, SamplingParams
-model_id = "launch/ThinkPRM-1.5B" # Replace with actual model ID on Hub
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 llm = LLM(model=model_id, max_model_len=16384)
@@ -69,7 +69,7 @@ problem = "Solve for x: 2x + 3 = 7"
 prefix = "Step 1: Subtract 3 from both sides: 2x = 4
 Step 2: Divide by 2: x = 1"
-# Format the prompt
 prompt = f"""You are given a math problem and a proposed step-by-step solution:
 [Math Problem]

 from transformers import AutoModelForCausalLM, AutoTokenizer
 from vllm import LLM, SamplingParams
+model_id = "launch/ThinkPRM-1.5B"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 llm = LLM(model=model_id, max_model_len=16384)
 prefix = "Step 1: Subtract 3 from both sides: 2x = 4
 Step 2: Divide by 2: x = 1"
+# Format the prompt -- use this exact format to get the best performance
 prompt = f"""You are given a math problem and a proposed step-by-step solution:
 [Math Problem]