Qwen3-0.6B Full-Precision + W8A8 Quantized MCQA Model

Repository: Kikinoking/MNLP_M2_quantized_model

This is a fine-tuned Qwen-3-0.6B causal-LM, trained on a concatenation of multiple MCQA datasets and then quantized to 8-bit weights and activations using the compressed-tensors format. It is designed for multiple-choice QA tasks, evaluated with the LightEval EPFL MNLP suite.

Model Details

Base architecture: Qwen-3 (0.6B parameters)
Pretrained checkpoint: Qwen/Qwen3-0.6B-Base
Fine-tuning data sources:
- ScienceQA
- QASC
- OpenBookQA
- MathQA
- CommonsenseQA
- MCQA prompts generated via ChatGPT (labeled M1_chatgpt)
Dataset split: 95% train / 5% validation
Tokenization:
- AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B-Base")
- Left padding, EOS token as pad_token
- Sequence length capped at 2048 tokens

Quantization

Method: compressed-tensors / naive-quantized
Precision: 8-bit weights + 8-bit activations
Layers kept in FP32: Language modeling head
Checkpoint: Compatible with CPU and GPU inference

Evaluation

Tested using LightEval EPFL MNLP on the MCQA task:

lighteval accelerate   --eval-mode lighteval   --save-details   --override-batch-size 8   --custom-tasks community_tasks/mnlp_mcqa_evals.py   --output-dir out/lighteval_quant   model_configs/quantized_model.yaml   "community|mnlp_mcqa_evals|0|0"

Results:

    Accuracy: 0.30 ± 0.15

    Normalized Accuracy: 0.30 ± 0.15

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained(
    "Kikinoking/MNLP_M2_quantized_model", trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    "Kikinoking/MNLP_M2_quantized_model",
    trust_remote_code=True,
    device_map="auto",
)

License

    Being a 0.6B-parameter model, it may struggle with very long or ambiguous queries.

    Quantization can introduce a slight drop in accuracy (~5–10%).

    License: CC BY-NC 4.0 (inherits from the base Qwen-3 license)