📦 What's Included
Table with columns: Variant, Size, Format, Best For| 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
ollama run nachikethreddyy/qwen3.5-8b-distilled-GGUF:F16
llama.cpp
# Install
brew install llama.cpp
# Run
llama-cli -hf nachikethreddyy/qwen3.5-8b-distilled-GGUF:F16
LM Studio
- Download LM Studio
- Search:
nachikethreddyy/qwen3.5-8b-distilled-GGUF
- Download & run!
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