⚠️ CRITICAL: Ollama Inference Flag Required
If you serve this model via Ollama with the qwen3.5 RENDERER, you MUST pass
"think": falsein the/api/chatrequest body for chat / instruction following / tool use.Without this flag, 25-46% of requests will return empty answers due to renderer-injected
<think>tags. Seecudabenchmarktest/r9-research-framework/_OLLAMA_INFERENCE_WARNING.mdfor full details.
Qwen3.5-9B R5 Research (GGUF)
Fine-tuned Qwen3.5-9B with distilled reasoning. R5 was the first round using production-quality data sources and achieved 84.2% on diverse stochastic eval. Superseded by R7 (86.8%).
For Ollama: ollama run robit/qwen3.5-9b-r5-research:q4km
Capabilities
- Thinking — structured reasoning in
<think>blocks - Tool calling — structured
tool_callsvia Ollama API - Instruction following — concise answers, format constraints, system prompt adherence
Eval Results
| Benchmark | Score |
|---|---|
| Diverse stochastic eval (38 tests, 9 categories) | 84.2% |
| Base qwen3.5:9b on same eval | 79.0% |
Training
- Base model: Qwen/Qwen3.5-9B
- Method: LoRA SFT (r=32, alpha=64, LR=1e-4, 1 epoch)
- Data: 4122 samples from:
- bespokelabs/Bespoke-Stratos-17k — DeepSeek-R1 reasoning traces
- allenai/tulu-3-sft-mixture — instruction diversity
- Open-Orca/SlimOrca — curated GPT-4 instructions
- Training suite: robit-man/fine_tuning_suite
Models
| Format | Link |
|---|---|
| Ollama (Q4_K_M) | robit/qwen3.5-9b-r5-research |
| Ollama Vision (Q4_K_M) | robit/qwen3.5-9b-r5-vision |
| Successor: R7 (FP16) | cudabenchmarktest/qwen3.5-9b-r7-research |
| Successor: R7 Vision (FP16) | cudabenchmarktest/qwen3.5-9b-r7-research-vision |
License
Apache 2.0 (inherited from Qwen3.5-9B). Training data licenses vary by source.