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Model: ermiaazarkhalili/Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- unsloth
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- qwen3
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- sft
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- fine-tuned
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- trl
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- lora
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- qlora
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- text-generation
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- reasoning
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- conversational
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base_model: unsloth/qwen3-8b-unsloth-bnb-4bit
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datasets:
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- ermiaazarkhalili/claude-reasoning-distillation
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model-index:
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- name: Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth
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results: []
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---
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# Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth
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This model is a fine-tuned version of [Qwen3-8B (Unsloth 4-bit)](https://huggingface.co/unsloth/qwen3-8b-unsloth-bnb-4bit) optimized for **reasoning distillation (chain-of-thought)** using [Unsloth](https://github.com/unslothai/unsloth) for **2x faster training** and **60% less VRAM**.
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Trained on the [claude-reasoning-distillation](https://huggingface.co/datasets/ermiaazarkhalili/claude-reasoning-distillation) dataset, which contains 10,477 samples of Claude's reasoning traces with `<think>` blocks for chain-of-thought learning.
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## Overview
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| Property | Value |
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|----------|-------|
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| **Developed by** | [ermiaazarkhalili](https://huggingface.co/ermiaazarkhalili) |
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| **License** | APACHE-2.0 |
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| **Language** | English |
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| **Base Model** | [Qwen3-8B (Unsloth 4-bit)](https://huggingface.co/unsloth/qwen3-8b-unsloth-bnb-4bit) |
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| **Model Size** | 8B parameters |
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| **Training Framework** | [Unsloth](https://github.com/unslothai/unsloth) + [TRL](https://github.com/huggingface/trl) |
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| **Training Method** | SFT with QLoRA (4-bit) |
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| **Context Length** | 2,048 tokens |
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| **GGUF Available** | [Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF](https://huggingface.co/ermiaazarkhalili/Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF) |
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## Training Configuration
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### SFT + LoRA Settings
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| Parameter | Value |
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|-----------|-------|
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| Unsloth Class | `FastLanguageModel` |
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| Chat Template | built-in Qwen3 |
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| Learning Rate | 2e-4 |
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| Batch Size | 1 per device |
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| Gradient Accumulation | 8 steps |
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| Effective Batch Size | 8 |
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| Max Steps | 1 epoch (full dataset) |
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| Optimizer | AdamW 8-bit |
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| LR Scheduler | Linear |
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| Warmup Steps | 5 |
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| Precision | Auto (BF16/FP16) |
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| Gradient Checkpointing | Enabled (Unsloth optimized) |
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| Seed | 3407 |
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### LoRA Configuration
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| Parameter | Value |
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|-----------|-------|
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| LoRA Rank (r) | 16 |
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| LoRA Alpha | 16 |
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| LoRA Dropout | 0 |
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| Quantization | 4-bit QLoRA |
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| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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### Dataset
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| Property | Value |
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|----------|-------|
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| Dataset | [Claude Reasoning Distillation](https://huggingface.co/datasets/ermiaazarkhalili/claude-reasoning-distillation) |
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| Training Samples | 10,477 |
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| Format | Messages with `thinking` field for chain-of-thought |
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### Hardware
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| Property | Value |
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|----------|-------|
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| GPU | NVIDIA H100 80GB HBM3 (MIG 3g.40gb slice) |
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| Cluster | DRAC Fir (Compute Canada) |
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| Execution | [Papermill](https://github.com/nteract/papermill) on SLURM |
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### Training Outcome
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| Metric | Value |
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|--------|-------|
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| SLURM Job ID | `36885901` |
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| Runtime | 40m 30s (2430s) |
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| Final Training Loss | 0.8753 |
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| Peak VRAM | 14.23 GB |
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| GPU | H100 80GB HBM3 (MIG 3g.40gb) |
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## Usage
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### Quick Start (Transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "ermiaazarkhalili/Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth"
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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=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "Solve step by step: What is the sum of the first 10 prime numbers?"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
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response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print(response)
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```
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### Using with Unsloth (Fastest)
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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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"ermiaazarkhalili/Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth",
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max_seq_length=2048,
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load_in_4bit=True,
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)
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```
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### 4-bit Quantized Inference
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```python
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from transformers import AutoModelForCausalLM, BitsAndBytesConfig
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import torch
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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)
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model = AutoModelForCausalLM.from_pretrained(
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"ermiaazarkhalili/Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth",
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quantization_config=quantization_config,
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device_map="auto",
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)
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```
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## GGUF Versions
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Quantized GGUF versions for CPU and edge inference are available at:
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**[Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF](https://huggingface.co/ermiaazarkhalili/Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF)**
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| Format | Description |
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|--------|-------------|
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| `Q4_K_M` | Recommended — good balance of quality and size |
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| `Q5_K_M` | Higher quality, slightly larger |
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| `Q8_0` | Near-lossless, largest GGUF size |
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### Using with Ollama
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```bash
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ollama pull hf.co/ermiaazarkhalili/Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF:Q4_K_M
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ollama run hf.co/ermiaazarkhalili/Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF:Q4_K_M "Solve step by step: What is the sum of the first 10 prime numbers?"
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```
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### Using with llama.cpp
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```bash
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./llama-cli -m Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth-Q4_K_M.gguf -p "Solve step by step: What is the sum of the first 10 prime numbers?" -n 512
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```
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## Limitations
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- **Language**: Primarily trained on English data
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- **Knowledge Cutoff**: Limited to base model's training data cutoff
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- **Hallucinations**: May generate plausible-sounding but incorrect information
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- **Context Length**: Fine-tuned with 2,048 token context window
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- **Safety**: Not extensively safety-tuned; use with appropriate guardrails
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## Training Framework Versions
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| Package | Version |
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|---------|---------|
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| Unsloth | 2026.4.4 |
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| TRL | 0.24.0 |
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| Transformers | 5.5.0 |
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| PyTorch | 2.9.0 |
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| Datasets | 4.3.0 |
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| PEFT | 0.18.1 |
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| BitsAndBytes | 0.49.2 |
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## Citation
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```bibtex
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@misc{ermiaazarkhalili_qwen3_8b_sft_claude_opus_reasoning_unsloth,
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author = {ermiaazarkhalili},
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title = {Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth: Fine-tuned Qwen3-8B (Unsloth 4-bit) with Unsloth},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/ermiaazarkhalili/Qwen3-8B-SFT-Claude-Opus-Reasoning-Unsloth}}
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}
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```
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## Acknowledgments
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- [Unsloth](https://github.com/unslothai/unsloth) for 2x faster fine-tuning
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- Base model developers (unsloth)
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- [Hugging Face TRL Team](https://github.com/huggingface/trl) for the training library
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- [Claude Reasoning Distillation](https://huggingface.co/datasets/ermiaazarkhalili/claude-reasoning-distillation) dataset
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- [Compute Canada / DRAC](https://alliancecan.ca/) for HPC resources
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