---
language:
  - en
license: apache-2.0
library_name: transformers
base_model: Qwen/Qwen3-8B
tags:
  - text-generation
  - fine-tune
  - coding
  - gguf
  - llama.cpp
  - ollama
  - lm-studio
pipeline_tag: text-generation
inference: true
model_creator: nachikethreddyy
---

# Qwen3.5-8B Distilled - GGUF Format

Fine-tuned **Qwen3.5-8B** for software engineering & coding tasks. **GGUF-optimized** version for local inference.

## 📦 What's Included

| Variant | Size | Format | Best For |
|---------|------|--------|----------|
| **Full Precision (BF16)** | 16.39 GB | Safetensors | Maximum quality, research |
| **Q8 Quantized** | 8.8 GB | Safetensors | Balanced speed/quality |
| **GGUF F16** | 15.3 GB | GGUF | Ollama, llama.cpp, LM Studio |

## 🚀 Quick Start

### Ollama
```bash
ollama run nachikethreddyy/qwen3.5-8b-distilled-GGUF:F16
```

### llama.cpp
```bash
# Install
brew install llama.cpp

# Run
llama-cli -hf nachikethreddyy/qwen3.5-8b-distilled-GGUF:F16
```

### LM Studio
1. Download [LM Studio](https://lmstudio.ai)
2. Search: `nachikethreddyy/qwen3.5-8b-distilled-GGUF`
3. Download & run!

### Transformers (Full/Q8)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "nachikethreddyy/qwen3.5-8b-distilled-GGUF",
    device_map="auto"
)
```

## 📊 Training Details
- **Base:** Qwen/Qwen3-8B
- **Method:** LoRA Fine-tuning (r=16, alpha=32)
- **Data:** 256 coding examples
- **Framework:** MLX
- **Iterations:** 1600

## 📄 License
Apache 2.0 (inherited from Qwen/Qwen3-8B)

---
**For MLX/Apple Silicon:** See [qwen3.5-8b-distilled-MLX](https://huggingface.co/nachikethreddyy/qwen3.5-8b-distilled-MLX)