Kikinoking
/

MNLP_M2_quantized_model

@@ -1,199 +1,79 @@
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 ---
+tags:
+- causal-lm
+- qwen
+- fine-tuned
+- quantized
+- mnlp
 ---
+# Qwen3-0.6B Full-Precision + W8A8 Quantized MCQA Model
+**Repository:** [Kikinoking/MNLP_M2_quantized_model](https://huggingface.co/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:
+```bash
+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)