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

Libraries

How to use andresnowak/Qwen3-0.6B-instruction-finetuned-MCQA with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/andresnowak/Qwen3-0.6B-instruction-finetuned-MCQA

SGLang

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

Unsloth Studio

How to use andresnowak/Qwen3-0.6B-instruction-finetuned-MCQA 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-instruction-finetuned-MCQA 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-instruction-finetuned-MCQA 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-instruction-finetuned-MCQA 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-instruction-finetuned-MCQA",
    max_seq_length=2048,
)

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

Qwen3-0.6B-instruction-finetuned-MCQA

This model is a fine-tuned version of andresnowak/Qwen3-0.6B-instruction-finetuned on an unknown dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

This model was trained with the same methodology as https://huggingface.co/andresnowak/MNLP_M2_mcqa_model, where we only do a feedforward on the prompt we get the last logit token and we do cross entropy loss on that token and the 4 options of the question (so the idea is that we want to maximize the likelihood of the model of printing the correct letter to the question)

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2

Training 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 is:

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	61.39%	59.96%
ARC Easy	79.43%	76.51%
GPQA	32.59%	28.57%
Math QA	24.69%	24.80%
MCQA Evals	41.82%	39.22%
MMLU	52.11%	52.11%
MMLU Pro	15.41%	14.31%
MuSR	51.06%	48.41%
NLP4Education	44.14%	42.73%
Overall	44.74%	42.96%

The tests where done with this prompt (And only MusR used a different one where you add the Question: and Narrative: )

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]