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Model: NhatCuong22/Qwen3-8B-OpusReasoning Source: Original Platform
2026-05-12 10:45:21 +08:00
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+---
+language:
+- en
+license: apache-2.0
+base_model: unsloth/Qwen3-8B
+tags:
+- reasoning
+- qwen3
+- lora
+- unsloth
+- distillation
+- claude-opus
+- chain-of-thought
+datasets:
+- Crownelius/Opus-4.6-Reasoning-3300x
+- Jackrong/Qwen3.5-reasoning-700x
+model-index:
+- name: Qwen3-8B-OpusReasoning
+  results: []
+pipeline_tag: text-generation
+---
+
+# Qwen3-8B-OpusReasoning
+
+## Model Overview
+
+A reasoning-enhanced version of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B), fine-tuned via supervised knowledge distillation from **Claude Opus 4.6** reasoning traces.
+
+The goal is not token-level imitation of Opus output, but transfer of its **reasoning structure and problem-solving style** into a compact 8B model that can run locally. The model outputs structured chain-of-thought inside `<think>...</think>` tags before generating the final answer, following the Qwen3 thinking-mode convention.
+
+- **Base model:** [unsloth/Qwen3-8B](https://huggingface.co/unsloth/Qwen3-8B)
+- **Teacher model:** Claude Opus 4.6 (reasoning traces, distilled)
+- **Training type:** Supervised Fine-Tuning (SFT) + LoRA → merged bf16
+- **Framework:** [Unsloth](https://github.com/unslothai/unsloth) 2026.4.5 + TRL SFTTrainer
+- **Precision:** bfloat16
+- **Hardware:** 1x NVIDIA A100-SXM4-80GB
+
+## Training Data
+
+| Dataset | Samples | Role |
+|---|---|---|
+| [Crownelius/Opus-4.6-Reasoning-3300x](https://huggingface.co/datasets/Crownelius/Opus-4.6-Reasoning-3300x) | 3,300 | Main distillation — Claude Opus 4.6 reasoning traces |
+| [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) | 700 | Auxiliary — supporting reasoning diversity |
+| **Total** | **4,000** | |
+
+### Data Characteristics
+
+- Long-form chain-of-thought supervision (`<think>...</think>`)
+- Diverse reasoning domains: math, logic, code, analytical QA
+- High-quality Opus 4.6 teacher traces — carefully curated, no noisy labels
+- Conversation format compatible with Qwen3 chat template
+
+## Training Pipeline
+
+### LoRA Configuration
+
+| Parameter | Value |
+|---|---|
+| Rank (r) | 64 |
+| Alpha | 128 |
+| Dropout | 0.05 |
+| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+
+### Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Effective batch size | 1 × 16 = 16 |
+| Learning rate | 5e-5 |
+| LR scheduler | Cosine |
+| Epochs | 3 |
+| Max sequence length | 16,384 |
+| Optimizer | AdamW 8-bit |
+| Warmup ratio | 0.03 |
+| Weight decay | 0.01 |
+| Packing | Enabled |
+| Gradient checkpointing | Unsloth |
+
+### Training Results
+
+- **Final train loss:** 0.6953
+- **Runtime:** ~153 minutes on A100-80GB
+
+## Distillation Philosophy
+
+We distill **reasoning structure**, not surface tokens. Specifically, the model is encouraged to acquire:
+
+- **Explicit problem decomposition** — break complex questions into sub-goals
+- **Assumption checking** — state what's given, what's unknown, and verify constraints
+- **Step-by-step derivation** — one logical step per line, no skipped algebra
+- **Reflection & backtracking** — recognize dead-ends and revise rather than plow forward
+- **Clean answer construction** — separate `<think>` scratch work from the final user-facing answer
+
+This follows the "Claude Opus style" of reasoning — deliberative, self-critical, and structurally transparent.
+
+## Reasoning Scaffold (Learned Pattern)
+
+After fine-tuning, the model tends to produce reasoning traces with this shape:
+
+1. **Restate and parse the task** — identify exactly what is being asked
+2. **Plan** — list the approach or sub-problems
+3. **Work through each step** — show algebra, logic, or code reasoning explicitly
+4. **Verify** — sanity-check the intermediate results before committing
+5. **Construct the final answer** — separate, clean, user-facing summary
+
+## Expected Improvements
+
+In practice, the gain is not a dramatic capability jump over the base Qwen3-8B, but rather:
+
+- **Improved stability** in multi-step reasoning
+- **Structured, readable traces** instead of rambling CoT
+- **Better instruction adherence** when a problem has constraints
+- **Fewer hallucinated intermediate steps** thanks to Opus-style self-verification
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "NhatCuong22/Qwen3-8B-OpusReasoning"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="bfloat16",
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": "If a train travels 120km in 2 hours, stops for 30 minutes, then travels 90km in 1.5 hours, what is the average speed for the entire journey?"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+text += "<think>\n"  # Activate thinking mode
+
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=2048,
+    temperature=0.6,
+    top_p=0.95,
+    do_sample=True,
+)
+
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
+print(response)
+```
+
+### Unsloth (faster inference)
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "NhatCuong22/Qwen3-8B-OpusReasoning",
+    max_seq_length=16384,
+    dtype="bfloat16",
+)
+FastLanguageModel.for_inference(model)
+```
+
+### Recommended Generation Parameters
+
+| Parameter | Reasoning tasks | Creative tasks |
+|---|---|---|
+| temperature | 0.6 | 0.8 |
+| top_p | 0.95 | 0.95 |
+| max_new_tokens | 2048-4096 | 1024-2048 |
+| repetition_penalty | 1.0 | 1.05 |
+
+## Model Architecture
+
+| Parameter | Value |
+|---|---|
+| Parameters | ~8B |
+| Hidden size | 4,096 |
+| Layers | 36 |
+| Attention heads | 32 (8 KV heads, GQA) |
+| Intermediate size | 12,288 |
+| Max position embeddings | 40,960 |
+| Vocabulary size | 151,936 |
+| Precision | bfloat16 |
+
+## Evaluation
+
+Evaluated with [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) (5-shot, bf16, A100-80GB).
+
+### Results vs base Qwen3-8B
+
+| Benchmark | Metric | Qwen3-8B-OpusReasoning | Base Qwen3-8B | Δ |
+|---|---|---|---|---|
+| **MMLU** | accuracy | **75.40%** | 72.93% | **+2.47** ✅ |
+| **ARC-Challenge** | acc_norm | **65.87%** | 56.74% | **+9.13** ✅✅ |
+| **ARC-Challenge** | accuracy | 64.42% | — | — |
+| **HellaSwag** | acc_norm | **76.98%** | 74.91% | **+2.07** ✅ |
+| **HellaSwag** | accuracy | 58.32% | — | — |
+| **GSM8K** | exact_match (strict) | 86.20% | 88.60% | -2.40 |
+| **GSM8K** | exact_match (flexible) | 86.66% | — | — |
+
+### Analysis
+
+- **MMLU +2.47, ARC-Challenge +9.13, HellaSwag +2.07** — the model retains and slightly improves general knowledge and commonsense reasoning after reasoning-focused fine-tuning, without catastrophic forgetting.
+- **ARC-Challenge +9.13** is a strong signal that Opus-style structured reasoning transfers well to scientific reasoning tasks.
+- **GSM8K -2.4** is a minor regression, likely due to longer `<think>` traces being occasionally truncated by the default `max_gen_toks` — the model is still at ~87% on grade-school math.
+
+More rigorous reasoning benchmarks (MMLU-Pro, MATH-Hard, AIME, IFEval, MuSR) are being evaluated and will be added here.
+
+## Best Suited For
+
+- Mathematical problem solving (arithmetic, algebra, word problems)
+- Logical reasoning and deduction
+- Code generation with explanation
+- Multi-step analytical question answering
+- Instruction-following tasks with constraints
+- Offline / on-prem reasoning assistants (fits in 16GB VRAM at bf16)
+
+## Limitations & Intended Use
+
+- **Scale of supervision:** fine-tuned on only ~4K samples — gains are stylistic and structural, not broad knowledge expansion
+- **Hallucination risk:** reasoning traces may confidently cite non-existent facts; verify external claims
+- **Opus-style bias:** inherits tendencies of the teacher (e.g., verbosity, occasional over-hedging)
+- **Language:** primarily English training data
+- **Not verified for safety-critical use** — research and learning only
+- **Base model license constraints:** follow Qwen3 upstream license in commercial settings
+
+## Acknowledgments
+
+- [Qwen Team](https://huggingface.co/Qwen) — Qwen3-8B base model
+- [Unsloth](https://github.com/unslothai/unsloth) — efficient fine-tuning kernels
+- [Anthropic](https://www.anthropic.com/) — Claude Opus reasoning (teacher)
+- Dataset authors: [Crownelius](https://huggingface.co/Crownelius), [Jackrong](https://huggingface.co/Jackrong)
+
+## Citation
+
+```bibtex
+@misc{qwen3-8b-opusreasoning,
+  title        = {Qwen3-8B-OpusReasoning},
+  author       = {Vo Nhat Cuong},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/NhatCuong22/Qwen3-8B-OpusReasoning}}
+}
+```
+
+## License
+
+Apache 2.0 (inherits from Qwen3-8B upstream).