75 lines
1.7 KiB
Markdown
75 lines
1.7 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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base_model: Qwen/Qwen3-8B
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tags:
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- text-generation
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- fine-tune
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- coding
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- gguf
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- llama.cpp
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- ollama
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- lm-studio
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pipeline_tag: text-generation
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inference: true
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model_creator: nachikethreddyy
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---
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# Qwen3.5-8B Distilled - GGUF Format
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Fine-tuned **Qwen3.5-8B** for software engineering & coding tasks. **GGUF-optimized** version for local inference.
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## 📦 What's Included
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| Variant | Size | Format | Best For |
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|---------|------|--------|----------|
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| **Full Precision (BF16)** | 16.39 GB | Safetensors | Maximum quality, research |
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| **Q8 Quantized** | 8.8 GB | Safetensors | Balanced speed/quality |
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| **GGUF F16** | 15.3 GB | GGUF | Ollama, llama.cpp, LM Studio |
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## 🚀 Quick Start
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### Ollama
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```bash
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ollama run nachikethreddyy/qwen3.5-8b-distilled-GGUF:F16
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```
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### llama.cpp
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```bash
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# Install
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brew install llama.cpp
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# Run
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llama-cli -hf nachikethreddyy/qwen3.5-8b-distilled-GGUF:F16
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```
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### LM Studio
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1. Download [LM Studio](https://lmstudio.ai)
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2. Search: `nachikethreddyy/qwen3.5-8b-distilled-GGUF`
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3. Download & run!
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### Transformers (Full/Q8)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"nachikethreddyy/qwen3.5-8b-distilled-GGUF",
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device_map="auto"
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)
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```
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## 📊 Training Details
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- **Base:** Qwen/Qwen3-8B
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- **Method:** LoRA Fine-tuning (r=16, alpha=32)
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- **Data:** 256 coding examples
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- **Framework:** MLX
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- **Iterations:** 1600
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## 📄 License
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Apache 2.0 (inherited from Qwen/Qwen3-8B)
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---
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**For MLX/Apple Silicon:** See [qwen3.5-8b-distilled-MLX](https://huggingface.co/nachikethreddyy/qwen3.5-8b-distilled-MLX)
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