Qwen3.5-9B Claude Opus 4.6 Reasoning Distill β GGUF
GGUF quantizations of empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill, a reasoning-focused fine-tune of Qwen/Qwen3.5-9B.
This model was trained to produce detailed chain-of-thought reasoning inside <think> tags before giving its final answer, distilled from Claude Opus 4.6 and Qwen3.5 reasoning traces.
Quantizations
For most users, Q4_K_M or Q5_K_M is the sweet spot.
How to Use
llama.cpp
Ollama
LM Studio / GPT4All / Jan
Download the GGUF file of your choice and load it directly in the application.
Training Details
Method
- Stage 1 β SFT (Supervised Fine-Tuning): 3 epochs on ~13K examples teaching the model the
<think> reasoning format using QLoRA (4-bit, rank 64, alpha 128)
- Base model: Qwen/Qwen3.5-9B
- Hardware: RTX 5090 (32GB VRAM)
- Attention: SDPA
- Optimizer: Paged AdamW 8-bit
- Learning rate: 1e-4 with cosine schedule
- Effective batch size: 8 (batch 1 Γ gradient accumulation 8)
- Max sequence length: 4096
SFT Results
Best checkpoint (epoch 2) was selected via load_best_model_at_end.
Datasets
Output Format
The model outputs reasoning in <think> tags followed by its final answer:
About Empero AI
This model was developed by Empero AI. We build open-source AI tools and models focused on advancing reasoning capabilities in smaller, efficient language models.
License
This model inherits the Apache 2.0 license from Qwen3.5-9B.