Instructions to use alphadl/R1-Distill-0.6B-Qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alphadl/R1-Distill-0.6B-Qwen with Transformers:

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

pipe = pipeline("text-generation", model="alphadl/R1-Distill-0.6B-Qwen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alphadl/R1-Distill-0.6B-Qwen")
model = AutoModelForCausalLM.from_pretrained("alphadl/R1-Distill-0.6B-Qwen")
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 alphadl/R1-Distill-0.6B-Qwen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alphadl/R1-Distill-0.6B-Qwen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alphadl/R1-Distill-0.6B-Qwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alphadl/R1-Distill-0.6B-Qwen

SGLang

How to use alphadl/R1-Distill-0.6B-Qwen 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 "alphadl/R1-Distill-0.6B-Qwen" \
    --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": "alphadl/R1-Distill-0.6B-Qwen",
		"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 "alphadl/R1-Distill-0.6B-Qwen" \
        --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": "alphadl/R1-Distill-0.6B-Qwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alphadl/R1-Distill-0.6B-Qwen with Docker Model Runner:
```
docker model run hf.co/alphadl/R1-Distill-0.6B-Qwen
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for R1-Distill-0.6B-Qwen

This model is a fine-tuned version of Qwen/Qwen3-0.6B-Base on the open-r1/Mixture-of-Thoughts dataset. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="alphadl/R1-Distill-0.6B", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.18.0
Transformers: 4.52.3
Pytorch: 2.6.0
Datasets: 3.2.0
Tokenizers: 0.21.1

Evaluation

lighteval: 0.10.0

Model	Qwen3-0.6B-Base	R1-Distill-0.6B (Ours)
Math-500	38.2	41.0(`+2.8`)
GPQA Diamond	24.2	28.3(`+4.1`)

export VLLM_WORKER_MULTIPROC_METHOD=spawn # Required for vLLM
export NUMEXPR_MAX_THREADS=128 # Utilize all 128 cores for numerical computations

MODEL=data/R1-Distill-0.6B

# Evaluate the base model
# MODEL=Qwen/Qwen3-0.6B-Base

MODEL_ARGS="model_name=$MODEL,dtype=bfloat16,max_model_length=32768,gpu_memory_utilization=0.8,generation_parameters={max_new_tokens:8192,temperature:0.6,top_p:0.95}"
OUTPUT_DIR=data/evals/$MODEL

# Math 500  
TASK=math_500
lighteval vllm $MODEL_ARGS "lighteval|$TASK|0|0" \
    --use-chat-template \
    --output-dir $OUTPUT_DIR

# GPQA Diamond
TASK=gpqa:diamond
lighteval vllm $MODEL_ARGS "lighteval|$TASK|0|0" \
    --use-chat-template \
    --output-dir $OUTPUT_DIR

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Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for alphadl/R1-Distill-0.6B-Qwen

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

(600)

this model

Finetunes

1 model

Quantizations