Instructions to use TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k with Transformers:

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

pipe = pipeline("text-generation", model="TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k")
model = AutoModelForCausalLM.from_pretrained("TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k")
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 TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k

SGLang

How to use TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k 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 "TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k" \
    --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": "TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k",
		"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 "TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k" \
        --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": "TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k with Docker Model Runner:
```
docker model run hf.co/TMLR-Group-HF/Majority-Voting-Llama-3.2-3B-Instruct-DAPO14k
```

Improve model card: Add metadata, paper link, description, and sample usage

by nielsr HF Staff - opened Oct 11, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+83

-3

nielsr

Oct 11, 2025

This PR significantly enhances the model card for the Majority-Voting: Llama-3.2-3B-Instruct model.

The key improvements include:

Metadata: Adding pipeline_tag: text-generation for better discoverability, library_name: transformers to enable the automated "how to use" widget on the Hub (supported by config.json and internal usage in the provided snippet), and relevant tags such as llama, fine-tuned, and mathematical-reasoning.
Paper Link: A direct link to the paper "Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models" is added.
Expanded Description: A detailed overview of the model and the "Co-rewarding" framework, derived from the paper's abstract, is now included.
GitHub Repository Link: The existing GitHub link has been updated and made more prominent.
Sample Usage: A "How to use" section with a Python code snippet, directly sourced from the project's GitHub README, is added to provide immediate guidance on model inference. This snippet includes the literal \n characters as found in the original source, as per instructions.

These changes collectively make the model card more informative and user-friendly.

Improve model card: Add metadata, paper link, description, and sample usage4f20730d

resistz changed pull request status to merged Oct 11, 2025

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