codelion
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Qwen3-0.6B-accuracy-recovery-lora

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 ---
+base_model: Qwen/Qwen3-0.6B
+tags:
+- ellora
+- lora
+- quantization
+- accuracy-recovery
+- distillation
+- magpie
+- efficiency
+- peft
+- qwen2
+library_name: peft
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+inference: false
+model_type: qwen2
 ---
+# codelion/Qwen3-0.6B-accuracy-recovery-lora
+## 🎯 Accuracy Recovery LoRA Adapter
+This LoRA adapter helps recover accuracy when using INT4 quantized versions of Qwen/Qwen3-0.6B.
+It was trained using self-distillation with Magpie-generated data.
+## 📊 Performance Metrics
+- **Base Model**: Qwen/Qwen3-0.6B
+- **Quantization**: INT4 with NF4
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 128
+- **Training Samples**: 610
+- **Target Performance Gap**: <5% perplexity increase
+## 🔧 Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+# Load base model with quantization
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4"
+)
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-0.6B",
+    quantization_config=quantization_config,
+    device_map="auto"
+)
+# Load LoRA adapter
+model = PeftModel.from_pretrained(model, "codelion/Qwen3-0.6B-accuracy-recovery-lora")
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
+# Use the model
+inputs = tokenizer("Hello, how are you?", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+## 🧪 Training Details
+- **Method**: Self-distillation using Magpie data generation
+- **Framework**: PEFT + LoRA
+- **Loss Function**: Combined KL divergence + MSE loss
+- **Temperature**: 1.0
+- **Alpha (distillation weight)**: 0.01
+## 📈 Expected Benefits
+- ✅ Maintains accuracy close to FP16 baseline
+- ✅ ~75% reduction in memory usage
+- ✅ 2-3x faster inference than FP16
+- ✅ Easy to integrate with existing workflows
+## 🏷️ Related
+- **Dataset**: [codelion/Qwen3-0.6B-magpie](https://huggingface.co/datasets/codelion/Qwen3-0.6B-magpie)
+- **Base Model**: [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
+- **Framework**: [PEFT](https://github.com/huggingface/peft)
+---
+*This adapter is part of the [Ellora project](https://github.com/codelion/ellora) - standardized recipes for enhancing LLM capabilities.*