qwen3-8b-finetune-multit-thinking

Qwen3 8B — Italian Cultural Alignment [V2 — Thinking Only]

Qwen3 8B [V2] is a LoRA adapter fine-tuned on top of Qwen/Qwen3-8B to improve Italian cultural alignment using exclusively thinking-format (synthetic chain-of-thought) training data. It was trained on a thinking-converted version of the Mult-IT dataset and evaluated on the ITALIC benchmark. V2 is the second version in a series of experiments exploring how supervised fine-tuning data format affects both cultural performance and the chain-of-thought reasoning capabilities of Qwen3's hybrid-reasoning architecture.

Author: Maruf Bepary, King's College London
Research report: Alignment in Large Language Models

Note: Thinking mode is the primary use case. V2 was trained exclusively on thinking-format data. This substantially improved Qwen3's chain-of-thought (<think>) performance whilst leaving No Thinking mode near-baseline. Use Thinking mode (enable_thinking=True) to benefit from fine-tuning. See Key Finding below.

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Model Summary

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Intended Use

This model is intended for:

• Italian reasoning tasks — multiple-choice Q&A, cultural knowledge, and instruction following in Italian using chain-of-thought reasoning.
• Research — studying the effect of thinking-format SFT on hybrid-reasoning language models, and confirming the role of training data format in reasoning mode degradation.
• Benchmarking — comparing Italian cultural alignment across model sizes, training strategies, and inference modes.

Not recommended for:

• Tasks that require maximum No Thinking mode performance — use V1 or V3 for that.
• High-stakes or safety-critical applications.
• Languages other than Italian.

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Key Finding — Thinking Mode Recovery

Training Qwen3 exclusively on thinking-format (synthetic chain-of-thought) data is the inverse of V1. Where V1 boosted No Thinking (+3.60 pp) whilst collapsing Thinking (−15.16 pp), V2 boosts Thinking (+3.38 pp) whilst leaving No Thinking virtually unchanged (+0.10 pp):

This confirms that the training data format directly determines which inference mode benefits from SFT. The reasoning-mode collapse observed in V1 was caused entirely by the non-thinking data format, not by the LoRA fine-tuning process itself.

A notable additional result: V2 Thinking (77.87%) surpasses the baseline Thinking (74.49%) and approaches Qwen3 14B Thinking (78.78%) despite being an 8B model — demonstrating that targeted SFT compensates meaningfully for the size gap.

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Version Series

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Training Details

LoRA Configuration

Training Hyperparameters

Framework & Hardware

Training Dataset — Mult-IT (Thinking Format)

• Dataset: Mult-IT — Multiple Choice Questions on Multiple Topics in Italian
• Source: CALAMITA Shared Task @ CLiC-it 2024
• Language: Italian
• Size: ~21,108 training samples
• Format: JSONL, multiple-choice Q&A with synthetic CoT reasoning traces. Only those training samples answered correctly by the model were included (no non-thinking format examples).
• Reference: Mult-IT: Multiple Choice Questions on Multiple Topics in Italian (2024)

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ITALIC Benchmark Results

Benchmark: ITALIC (NAACL 2025) — Italian Culture-Aware Natural Language Benchmark
Format: Zero-shot, multiple-choice (12 categories, 10,000 questions)
System prompt: "Sei un assistente utile."

No Thinking Mode — V2 vs Baseline (virtually unchanged)

Thinking Mode — V2 vs Baseline (improved)

Comparison with Other Models (Thinking Mode, ITALIC Total)

All scores evaluated under identical zero-shot conditions on the ITALIC benchmark.

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Usage

✅ Thinking mode is recommended. Use enable_thinking=True to activate chain-of-thought reasoning and benefit from V2 fine-tuning. No Thinking mode also works but performance is near-baseline.

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
import re

base_model_id = "Qwen/Qwen3-8B"
adapter_id = "maruf-bepary/qwen3-8b-italian-v2-thinking"

# Load tokeniser and base model
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

# Example: Italian multiple-choice question
messages = [
    {"role": "system", "content": "Sei un assistente utile."},
    {
        "role": "user",
        "content": (
            "Qual è la capitale d'Italia?\n"
            "A) Milano\nB) Roma\nC) Napoli\nD) Torino\n\n"
            "Rispondi con la lettera della risposta corretta."
        ),
    },
]

# Apply chat template — enable thinking mode (recommended for V2)
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,   # <-- primary mode for V2
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=False,
        temperature=None,
        top_p=None,
    )

full_response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[-1]:],
    skip_special_tokens=True,
)

# Strip the <think>...</think> block to extract the final answer
final_answer = re.sub(r"<think>.*?</think>", "", full_response, flags=re.DOTALL).strip()
print(final_answer)
# Expected output: "B"

No Thinking mode is also available but yields near-baseline performance:

# No Thinking mode — near-baseline performance
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,  # near-baseline; thinking mode is preferred for V2
)

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Limitations

• No Thinking mode shows negligible improvement — thinking-only training does not improve direct (non-reasoning) inference. Use Thinking mode to benefit from fine-tuning.
• Morphology in No Thinking mode degraded slightly (−3.42 pp) — thinking-only training appears to have slightly diminished direct morphological recall without the reasoning pathway.
• Benchmark scope — evaluation was conducted solely on ITALIC; Italian cultural performance on other benchmarks (e.g. MMLU-IT, HellaSwag-IT) is unverified.
• Single-GPU training — training used one RTX 3090; larger batch sizes or multi-GPU configurations may yield different results.
• Dataset bias — Mult-IT is a multiple-choice dataset; the model may not generalise equally well to open-ended Italian generation tasks.

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References

Related resources:

• Research report: Alignment in Large Language Models
• Base model: Qwen/Qwen3-8B
• ITALIC benchmark: RiTA-nlp/ITALIC
• Mult-IT dataset: sapienzanlp/Mult-IT
• PEFT documentation: huggingface.co/docs/peft