Instructions to use metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO with Transformers:

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

pipe = pipeline("text-generation", model="metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO")
model = AutoModelForCausalLM.from_pretrained("metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO

SGLang

How to use metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO 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 "metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO" \
    --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": "metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO",
		"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 "metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO" \
        --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": "metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO with Docker Model Runner:
```
docker model run hf.co/metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO
```

Qwen3-0.6B-GRPO / README.md

metacognitive-behavioral-tuning

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metadata

base_model: Qwen/Qwen3-0.6B
language:
  - en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
  - metacognitive-behavioral-tuning
  - multi-hop-qa
  - reasoning
  - sft
  - grpo
  - GRPO

Qwen3-0.6B · GRPO

GRPO (main table, base+GRPO) — final checkpoint (SFT → GRPO). Base: Qwen/Qwen3-0.6B. Paper: Metacognitive Behavioral Tuning of Large Language Models for Multi-Hop Question Answering.

Method: GRPO: base Qwen3 trained directly with GRPO (no SFT stage).
Base model: Qwen/Qwen3-0.6B
Training: SFT (LR 1e-4, BS 128, HotpotQA) → GRPO
Benchmarks: HotpotQA (ID), MuSiQue / 2WikiMultiHopQA (OOD)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "metacognitive-behavioral-tuning/Qwen3-0.6B-GRPO"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto")