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

Libraries

How to use andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text 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_model_text")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text 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_model_text"
# 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_model_text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text 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_model_text" \
    --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_model_text",
		"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_model_text" \
        --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_model_text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text 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_model_text 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_model_text 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_model_text 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_model_text",
    max_seq_length=2048,
)

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

Model Card for Qwen3-0.6B-MNLP_mcqa_model_text

This model is a fine-tuned version of unsloth/Qwen3-0.6B-Base. 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_model_text", 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 by doing finetuning as a Seq2Seq MCQA method (so doing question\n Letter. Answer) starting from the Qwen3-0.6B-base model. And it was trained doing Lanugage modelling (Loss on whole prompt and completion)

environment:
  seed: 42

model:
  name: Qwen/Qwen3-0.6B-Base
  hub_model_id: andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text

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:
  learning_rate: 1e-5
  per_device_train_batch_size: 2
  per_device_eval_batch_size: 2
  gradient_accumulation_steps: 32
  num_train_epochs: 2
  weight_decay: 0.00
  warmup_ratio: 0.1
  max_grad_norm: 0.5
  linear_layers_max_grad_norm: 1.0

Framework versions

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

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.

Important Note on MCQA Evals Benchmark:

The performance on these benchmarks is as follows:

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	60.6%	60.5%
ARC Easy	78.6%	76.8%
GPQA	30.1%	29.0%
Math QA	29.3%	28.5%
MCQA Evals	41.9%	38.7%
MMLU	48.6%	48.6%
MMLU Pro	14.5%	13.6%
MuSR	47.6%	47.6%
NLP4Education	43.7%	41.0%
Overall	43.9%	42.7%

The tests where done with this prompt:

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]

Citations

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}}
}