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Model: NhatCuong22/Qwen3-8B-OpusReasoning Source: Original Platform
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---
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language:
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- en
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license: apache-2.0
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base_model: unsloth/Qwen3-8B
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tags:
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- reasoning
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- qwen3
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- lora
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- unsloth
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- distillation
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- claude-opus
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- chain-of-thought
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datasets:
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- Crownelius/Opus-4.6-Reasoning-3300x
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- Jackrong/Qwen3.5-reasoning-700x
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model-index:
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- name: Qwen3-8B-OpusReasoning
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results: []
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pipeline_tag: text-generation
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---
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# Qwen3-8B-OpusReasoning
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## Model Overview
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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.
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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.
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- **Base model:** [unsloth/Qwen3-8B](https://huggingface.co/unsloth/Qwen3-8B)
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- **Teacher model:** Claude Opus 4.6 (reasoning traces, distilled)
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- **Training type:** Supervised Fine-Tuning (SFT) + LoRA → merged bf16
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- **Framework:** [Unsloth](https://github.com/unslothai/unsloth) 2026.4.5 + TRL SFTTrainer
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- **Precision:** bfloat16
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- **Hardware:** 1x NVIDIA A100-SXM4-80GB
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## Training Data
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| Dataset | Samples | Role |
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|---|---|---|
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| [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 |
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| [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) | 700 | Auxiliary — supporting reasoning diversity |
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| **Total** | **4,000** | |
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### Data Characteristics
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- Long-form chain-of-thought supervision (`<think>...</think>`)
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- Diverse reasoning domains: math, logic, code, analytical QA
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- High-quality Opus 4.6 teacher traces — carefully curated, no noisy labels
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- Conversation format compatible with Qwen3 chat template
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## Training Pipeline
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### LoRA Configuration
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| Parameter | Value |
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|---|---|
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| Rank (r) | 64 |
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| Alpha | 128 |
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| Dropout | 0.05 |
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| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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### Hyperparameters
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| Parameter | Value |
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|---|---|
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| Effective batch size | 1 × 16 = 16 |
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| Learning rate | 5e-5 |
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| LR scheduler | Cosine |
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| Epochs | 3 |
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| Max sequence length | 16,384 |
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| Optimizer | AdamW 8-bit |
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| Warmup ratio | 0.03 |
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| Weight decay | 0.01 |
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| Packing | Enabled |
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| Gradient checkpointing | Unsloth |
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### Training Results
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- **Final train loss:** 0.6953
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- **Runtime:** ~153 minutes on A100-80GB
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## Distillation Philosophy
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We distill **reasoning structure**, not surface tokens. Specifically, the model is encouraged to acquire:
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- **Explicit problem decomposition** — break complex questions into sub-goals
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- **Assumption checking** — state what's given, what's unknown, and verify constraints
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- **Step-by-step derivation** — one logical step per line, no skipped algebra
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- **Reflection & backtracking** — recognize dead-ends and revise rather than plow forward
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- **Clean answer construction** — separate `<think>` scratch work from the final user-facing answer
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This follows the "Claude Opus style" of reasoning — deliberative, self-critical, and structurally transparent.
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## Reasoning Scaffold (Learned Pattern)
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After fine-tuning, the model tends to produce reasoning traces with this shape:
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1. **Restate and parse the task** — identify exactly what is being asked
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2. **Plan** — list the approach or sub-problems
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3. **Work through each step** — show algebra, logic, or code reasoning explicitly
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4. **Verify** — sanity-check the intermediate results before committing
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5. **Construct the final answer** — separate, clean, user-facing summary
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## Expected Improvements
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In practice, the gain is not a dramatic capability jump over the base Qwen3-8B, but rather:
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- **Improved stability** in multi-step reasoning
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- **Structured, readable traces** instead of rambling CoT
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- **Better instruction adherence** when a problem has constraints
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- **Fewer hallucinated intermediate steps** thanks to Opus-style self-verification
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## Usage
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "NhatCuong22/Qwen3-8B-OpusReasoning"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="bfloat16",
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device_map="auto",
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)
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messages = [
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{"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?"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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text += "<think>\n" # Activate thinking mode
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=2048,
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temperature=0.6,
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top_p=0.95,
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do_sample=True,
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)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
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print(response)
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```
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### Unsloth (faster inference)
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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"NhatCuong22/Qwen3-8B-OpusReasoning",
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max_seq_length=16384,
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dtype="bfloat16",
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)
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FastLanguageModel.for_inference(model)
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```
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### Recommended Generation Parameters
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| Parameter | Reasoning tasks | Creative tasks |
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|---|---|---|
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| temperature | 0.6 | 0.8 |
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| top_p | 0.95 | 0.95 |
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| max_new_tokens | 2048-4096 | 1024-2048 |
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| repetition_penalty | 1.0 | 1.05 |
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## Model Architecture
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| Parameter | Value |
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|---|---|
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| Parameters | ~8B |
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| Hidden size | 4,096 |
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| Layers | 36 |
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| Attention heads | 32 (8 KV heads, GQA) |
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| Intermediate size | 12,288 |
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| Max position embeddings | 40,960 |
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| Vocabulary size | 151,936 |
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| Precision | bfloat16 |
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## Evaluation
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Evaluated with [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) (5-shot, bf16, A100-80GB).
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### Results vs base Qwen3-8B
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| Benchmark | Metric | Qwen3-8B-OpusReasoning | Base Qwen3-8B | Δ |
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|---|---|---|---|---|
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| **MMLU** | accuracy | **75.40%** | 72.93% | **+2.47** ✅ |
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| **ARC-Challenge** | acc_norm | **65.87%** | 56.74% | **+9.13** ✅✅ |
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| **ARC-Challenge** | accuracy | 64.42% | — | — |
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| **HellaSwag** | acc_norm | **76.98%** | 74.91% | **+2.07** ✅ |
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| **HellaSwag** | accuracy | 58.32% | — | — |
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| **GSM8K** | exact_match (strict) | 86.20% | 88.60% | -2.40 |
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| **GSM8K** | exact_match (flexible) | 86.66% | — | — |
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### Analysis
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- **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.
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- **ARC-Challenge +9.13** is a strong signal that Opus-style structured reasoning transfers well to scientific reasoning tasks.
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- **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.
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More rigorous reasoning benchmarks (MMLU-Pro, MATH-Hard, AIME, IFEval, MuSR) are being evaluated and will be added here.
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## Best Suited For
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- Mathematical problem solving (arithmetic, algebra, word problems)
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- Logical reasoning and deduction
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- Code generation with explanation
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- Multi-step analytical question answering
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- Instruction-following tasks with constraints
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- Offline / on-prem reasoning assistants (fits in 16GB VRAM at bf16)
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## Limitations & Intended Use
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- **Scale of supervision:** fine-tuned on only ~4K samples — gains are stylistic and structural, not broad knowledge expansion
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- **Hallucination risk:** reasoning traces may confidently cite non-existent facts; verify external claims
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- **Opus-style bias:** inherits tendencies of the teacher (e.g., verbosity, occasional over-hedging)
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- **Language:** primarily English training data
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- **Not verified for safety-critical use** — research and learning only
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- **Base model license constraints:** follow Qwen3 upstream license in commercial settings
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## Acknowledgments
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- [Qwen Team](https://huggingface.co/Qwen) — Qwen3-8B base model
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- [Unsloth](https://github.com/unslothai/unsloth) — efficient fine-tuning kernels
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- [Anthropic](https://www.anthropic.com/) — Claude Opus reasoning (teacher)
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- Dataset authors: [Crownelius](https://huggingface.co/Crownelius), [Jackrong](https://huggingface.co/Jackrong)
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## Citation
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```bibtex
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@misc{qwen3-8b-opusreasoning,
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title = {Qwen3-8B-OpusReasoning},
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author = {Vo Nhat Cuong},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/NhatCuong22/Qwen3-8B-OpusReasoning}}
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}
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```
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## License
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Apache 2.0 (inherits from Qwen3-8B upstream).
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98
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for forward_message in messages %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- set message = messages[index] %}
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{%- set current_content = message.content if message.content is not none else '' %}
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{%- set tool_start = '<tool_response>' %}
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{%- set tool_start_length = tool_start|length %}
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{%- set start_of_message = current_content[:tool_start_length] %}
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{%- set tool_end = '</tool_response>' %}
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{%- set tool_end_length = tool_end|length %}
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{%- set start_pos = (current_content|length) - tool_end_length %}
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{%- if start_pos < 0 %}
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{%- set start_pos = 0 %}
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{%- endif %}
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{%- set end_of_message = current_content[start_pos:] %}
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{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set content = message.content %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in message.content %}
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{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
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{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
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{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
|
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{%- endif %}
|
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{%- if message.tool_calls %}
|
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
|
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{{- '\n' }}
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{%- endif %}
|
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
|
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
|
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
|
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
|
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{%- endif %}
|
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
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{{- '<|im_end|>\n' }}
|
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{%- endif %}
|
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{%- endif %}
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{%- endfor %}
|
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{%- if add_generation_prompt %}
|
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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72
config.json
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72
config.json
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{
|
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"architectures": [
|
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"Qwen3ForCausalLM"
|
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],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"bos_token_id": null,
|
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"torch_dtype": "bfloat16",
|
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"eos_token_id": 151645,
|
||||
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||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d7430e9138b76e93fb6f93462394d236b411111aef53cb421ba97d2691040cca
|
||||
size 11423114
|
||||
16
tokenizer_config.json
Normal file
16
tokenizer_config.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"is_local": false,
|
||||
"model_max_length": 40960,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- set message = messages[index] %}\n {%- set current_content = message.content if message.content is not none else '' %}\n {%- set tool_start = '<tool_response>' %}\n {%- set tool_start_length = tool_start|length %}\n {%- set start_of_message = current_content[:tool_start_length] %}\n {%- set tool_end = '</tool_response>' %}\n {%- set tool_end_length = tool_end|length %}\n {%- set start_pos = (current_content|length) - tool_end_length %}\n {%- if start_pos < 0 %}\n {%- set start_pos = 0 %}\n {%- endif %}\n {%- set end_of_message = current_content[start_pos:] %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}"
|
||||
}
|
||||
Reference in New Issue
Block a user