Instructions to use BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC with Transformers:

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

pipe = pipeline("text-generation", model="BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC")
model = AutoModelForCausalLM.from_pretrained("BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC

SGLang

How to use BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC 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 "BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC" \
    --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": "BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC",
		"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 "BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC" \
        --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": "BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC with Docker Model Runner:
```
docker model run hf.co/BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

The base of this model is Llama-3.2-3B-Instruct, using QReCC as the training data, and the training method is ConvSearch-R1.

The code is available here. Please refer to the paper here.

Downloads last month: 22

Safetensors

Model size

3B params

Tensor type

F32

Model tree for BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC

Quantizations

1 model

Paper for BeastyZ/Llama3.2-3B-ConvSearch-R1-QReCC

ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning

Paper • 2505.15776 • Published May 21, 2025 • 11