Instructions to use sergiopaniego/qwen3-1.7b-mbpp-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sergiopaniego/qwen3-1.7b-mbpp-grpo with Transformers:

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

pipe = pipeline("text-generation", model="sergiopaniego/qwen3-1.7b-mbpp-grpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sergiopaniego/qwen3-1.7b-mbpp-grpo")
model = AutoModelForCausalLM.from_pretrained("sergiopaniego/qwen3-1.7b-mbpp-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 sergiopaniego/qwen3-1.7b-mbpp-grpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sergiopaniego/qwen3-1.7b-mbpp-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": "sergiopaniego/qwen3-1.7b-mbpp-grpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sergiopaniego/qwen3-1.7b-mbpp-grpo

SGLang

How to use sergiopaniego/qwen3-1.7b-mbpp-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 "sergiopaniego/qwen3-1.7b-mbpp-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": "sergiopaniego/qwen3-1.7b-mbpp-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 "sergiopaniego/qwen3-1.7b-mbpp-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": "sergiopaniego/qwen3-1.7b-mbpp-grpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sergiopaniego/qwen3-1.7b-mbpp-grpo with Docker Model Runner:
```
docker model run hf.co/sergiopaniego/qwen3-1.7b-mbpp-grpo
```

qwen3-1.7b-mbpp-grpo

Qwen3-1.7B trained with GRPO inside an OpenEnv coding environment, using TRL. Built for Class 4 of the Training Agents series.

The model solves MBPP problems inside a live Python session. A run_python tool executes code in that session and state persists across calls, so the model can define a function, test it, read the error and fix it. The environment owns the reward: it runs the task's hidden tests in the same session and returns the fraction that pass, which is why the training script has no reward_funcs at all.

Results

Fraction of hidden tests passed on the 257 problems of MBPP sanitized/test, which never appear in training. Both models were scored in the same pass, so the difference is not comparing across eval runs:

Model	Score
Qwen/Qwen3-1.7B (base)	0.518
this model	0.582

The same run also scored a fixed set of 64 held-out problems every 5 steps during training, which is the curve that actually shows the learning:

Instrument	Points	Rise	t
`train/reward`	42	+0.092 ± 0.065	1.41, not significant
`eval/reward`	10	0.541 → 0.635	5.18

The training reward looks flat because each GRPO step here scores only 4 problems, and MBPP problem difficulty varies far more than the model improves over one run. Step-to-step noise is 0.213 against an effect of about +0.06, so that curve has no power to resolve it. Watch eval/reward.

Dashboard: trackio-training-agents-4, run mbpp-grpo. The weights, the curve and the score above all come from that one run.

Reproducing it

hf jobs uv run --flavor a100-large --timeout 90m -s HF_TOKEN \
  -e VLLM_USE_FLASHINFER_SAMPLER=0 --with "trl[vllm]" \
  "https://huggingface.co/sergiopaniego/rl-envs-youtube-livestream-4-scripts/resolve/main/train_coding_whitebox_eval.py" \
  -- --model Qwen/Qwen3-1.7B \
     --env-url https://huggingface.co/proxy/sergiopaniego-coding-env.hf.space \
     --trackio-space-id sergiopaniego/trackio-training-agents-4 \
     --push-to-hub --hub-model-id <your-username>/qwen3-1.7b-mbpp-grpo

170 train problems (MBPP sanitized train + validation + prompt, none of them in the eval split), 42 steps, 4 problems per step, 4 generations each, lr 1e-6, about 60 minutes on one A100.

Scripts, with the eval script and the sizing gotchas: rl-envs-youtube-livestream-4-scripts
Environment: coding-env

What to know before you read the numbers

The dataset has a ceiling of 0.918, not 1.0. Running MBPP's own reference solutions inside this environment's sandbox, 14 of the 170 training problems are impossible: 11 need imports outside the executor's allowlist, three hit interpreter limitations.
About 32% of the reward is a test the model was shown. The "Example test" in the prompt is test_list[0], and that same test is one of the roughly 3.2 tests that get scored, in all 170 problems.
The size of the improvement moves between runs. Two runs of this exact config gave held-out rises of +0.095 and +0.118, both at t ~ 5.2, and 257-problem deltas of +0.064 and +0.100. The rise reproduces, its magnitude moves by about a third. Do not read one run's number as the number.

Usage

from transformers import pipeline

generator = pipeline("text-generation", model="sergiopaniego/qwen3-1.7b-mbpp-grpo", device="cuda")
print(generator("Write a python function to find the first repeated character in a string.")[0]["generated_text"])

The model was trained to work through a tool in a live session, so it is at its best driven the way it was trained, with run_python available and the hidden tests scoring the session afterwards. The snippet above is a smoke test, not the intended use.