Instructions to use andresnowak/Qwen3-0.6B-MNLP_mcqa_rl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andresnowak/Qwen3-0.6B-MNLP_mcqa_rl with Transformers:

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

pipe = pipeline("text-generation", model="andresnowak/Qwen3-0.6B-MNLP_mcqa_rl")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andresnowak/Qwen3-0.6B-MNLP_mcqa_rl")
model = AutoModelForCausalLM.from_pretrained("andresnowak/Qwen3-0.6B-MNLP_mcqa_rl")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use andresnowak/Qwen3-0.6B-MNLP_mcqa_rl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andresnowak/Qwen3-0.6B-MNLP_mcqa_rl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andresnowak/Qwen3-0.6B-MNLP_mcqa_rl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/andresnowak/Qwen3-0.6B-MNLP_mcqa_rl

SGLang

How to use andresnowak/Qwen3-0.6B-MNLP_mcqa_rl 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 "andresnowak/Qwen3-0.6B-MNLP_mcqa_rl" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andresnowak/Qwen3-0.6B-MNLP_mcqa_rl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "andresnowak/Qwen3-0.6B-MNLP_mcqa_rl" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andresnowak/Qwen3-0.6B-MNLP_mcqa_rl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use andresnowak/Qwen3-0.6B-MNLP_mcqa_rl with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for andresnowak/Qwen3-0.6B-MNLP_mcqa_rl to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for andresnowak/Qwen3-0.6B-MNLP_mcqa_rl to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for andresnowak/Qwen3-0.6B-MNLP_mcqa_rl to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="andresnowak/Qwen3-0.6B-MNLP_mcqa_rl",
    max_seq_length=2048,
)

Docker Model Runner
How to use andresnowak/Qwen3-0.6B-MNLP_mcqa_rl with Docker Model Runner:
```
docker model run hf.co/andresnowak/Qwen3-0.6B-MNLP_mcqa_rl
```

Model Card for Qwen3-0.6B-MNLP_mcqa_rl

This model is a fine-tuned version of andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text. 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="andresnowak/Qwen3-0.6B-MNLP_mcqa_rl", 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 GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models. And the methodology was Starting from the andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text, that was trained to output [Letter]. [Answer] the model was then trained again for 2 epochs on the same dataset but now with RLVR, if we find [Letter]. in the output we give $1.0$ and if not we give $-1.0$ (it was a very simple verifiable reward).

And the arguments used where:

environment:
  seed: 42

model:
  name: andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text
  hub_model_id: andresnowak/Qwen3-0.6B-MNLP_mcqa_rl

dataset_train:
  - name: andresnowak/MNLP_MCQA_dataset
    config: train
    subset_name: math_qa
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: ScienceQA
    config: train
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: mmlu-auxiliary-train-auto-labelled
    config: train
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: ai2_arc_challenge
    config: train
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: ai2_arc_easy
    config: train
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: medmcqa
    config: train
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: openbookqa
    config: train
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: sciq
    config: train

dataset_validation:
  - name: andresnowak/MNLP_MCQA_dataset
    config: validation
    subset_name: math_qa
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: ScienceQA
    config: validation
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: mmlu
    config: validation
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: ai2_arc_challenge
    config: validation
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: ai2_arc_easy
    config: validation
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: medmcqa
    config: validation
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: openbookqa
    config: validation
  - name: andresnowak/MNLP_MCQA_dataset
    subset_name: sciq
    config: validation

dataset_mmlu:
  - name: cais/mmlu
    config: validation
    subjects: ["abstract_algebra", "anatomy", "astronomy", "college_biology", "college_chemistry", "college_computer_science", "college_mathematics", "college_physics", "computer_security", "conceptual_physics", "electrical_engineering", "elementary_mathematics", "high_school_biology",  "high_school_chemistry", "high_school_computer_science", "high_school_mathematics", "high_school_physics", "high_school_statistics", "machine_learning"]


training:
  output_dir: ./output
  logging_dir: ./logs
  resume_dir: None
  report_to: wandb
  learning_rate: 1e-5
  per_device_train_batch_size: 8
  per_device_eval_batch_size: 8
  gradient_accumulation_steps: 8 # to get effective 64
  num_train_epochs: 1
  weight_decay: 0.00
  warmup_ratio: 0.05
  max_grad_norm: 1.0
  num_generations: 4
  completion_length: 100
  beta: 0.2

Evaluation Results

The model was evaluated on a suite of Multiple Choice Question Answering (MCQA) benchmarks (on its validation and test sets repsectively for each one), and NLP4education is only the approximated 1000 question and answers given to use.

The performance on the MCQA benchmarks after RL fine-tuning is as follows (This model has a very good performance on Math QA):

First evaluation: The tests where done with this prompt (type 5):

This question assesses challenging STEM problems as found on graduate standardized tests. Carefully evaluate the options and select the correct answer.

---
[Insert Question Here]
---
[Insert Choices Here, e.g.:
A. Option 1
B. Option 2
C. Option 3
D. Option 4]
---

Your response should include the letter and the exact text of the correct choice.
Example: B. Entropy increases.
Answer:

And the teseting was done on [Letter]. [Text answer]

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	64.3%	63.6%
ARC Easy	82.6%	81.9%
GPQA	33.0%	31.7%
Math QA	35.2%	34.6%
MCQA Evals	42.7%	41.0%
MMLU	49.4%	49.4%
MMLU Pro	15.1%	14.7%
MuSR	49.1%	47.0%
NLP4Education	47.2%	45.8%
Overall	46.5%	45.5%

Second evaluation: (type 0)

The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.

---
*[Insert Question Here]*
---
*[Insert Choices Here, e.g.:*
*A. Option 1*
*B. Option 2*
*C. Option 3*
*D. Option 4]*
---
Answer:

And the teseting was done on [Letter]. [Text answer]

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	66.62%	66.08%
ARC Easy	84.25%	82.04%
GPQA	29.69%	28.35%
Math QA	34.86%	33.50%
MCQA Evals	44.42%	40.52%
MMLU	49.29%	49.29%
MMLU Pro	16.81%	17.04%
MuSR	49.07%	46.96%
NLP4Education	49.73%	46.33%
Overall	47.19%	45.57%

Third evaluation: (type 2)


This is part of an assessment on graduate-level science, technology, engineering, and mathematics (STEM) concepts. Each question is multiple-choice and requires a single correct answer.

---
*[Insert Question Here]*
---
*[Insert Choices Here, e.g.:*
*A. Option 1*
*B. Option 2*
*C. Option 3*
*D. Option 4]*
---
For grading purposes, respond with: [LETTER]. [VERBATIM TEXT]
Example: D. Planck constant
Your Response:

And the teseting was done on [Letter]. [Text answer]

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	40.31%	40.31%
ARC Easy	56.21%	56.21%
GPQA	23.66%	23.66%
Math QA	25.92%	25.92%
MCQA Evals	33.12%	33.12%
MMLU	49.29%	49.29%
MMLU Pro	14.01%	14.01%
MuSR	49.21%	49.21%
NLP4Education	34.71%	34.71%
Overall	36.27%	36.27%

First evaluation [Letter]: (type 0)

The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.

---
*[Insert Question Here]*
---
*[Insert Choices Here, e.g.:*
*A. Option 1*
*B. Option 2*
*C. Option 3*
*D. Option 4]*
---
Answer:

And the teseting was done on [Letter]

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	66.62%	66.62%
ARC Easy	84.25%	84.25%
GPQA	27.23%	27.23%
Math QA	34.93%	34.93%
MCQA Evals	44.42%	44.42%
MMLU	49.29%	49.29%
MMLU Pro	17.26%	17.26%
MuSR	49.21%	49.21%
NLP4Education	50.08%	50.08%
Overall	47.03%	47.03%

Framework versions

TRL: 0.15.2
Transformers: 4.51.3
Pytorch: 2.5.1+cu121
Datasets: 3.6.0
Tokenizers: 0.21.0

Citations

Cite GRPO as:

@article{zhihong2024deepseekmath,
    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
    year         = 2024,
    eprint       = {arXiv:2402.03300},
}

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}