DeepSeek-V4-Pro-Qwen3.5-9B

base_model: unsloth/Qwen3.5-9B tags:

text-generation-inference
transformers
unsloth
qwen3_5
reasoning
distillation
deepseek
sft
rl
gspo
math
stem
tool-use
function-calling
mtp license: apache-2.0 language:
en
zh
ko
ja
es
ru pipeline_tag: text-generation

Model files

This repository contains the full model checkpoint. For the MTP GGUF variants and vision projector, see DeepSeek-V4-Pro-Qwen3.5-9B-MTP-GGUF.

🤝 Collaboration & Training Support

🔬 1. Distillation & Training Data

📊 2. Benchmark Results

2.1 GSM8K

The NVFP4 checkpoint was evaluated on GSM8K four times, and the resulting scores were averaged. Its four-run mean accuracy is 94.50% under vLLM. In the comparison supplied with this release, the 9B student slightly exceeds the reported DeepSeek-V4-Pro teacher result and approaches the Llama-3.3-70B-Instruct reference.

2.2 MMLU-Pro: Math, Physics & Chemistry

Each model was evaluated on 500 questions from each of three MMLU-Pro subsets—Math, Physics, and Chemistry—for 1,500 questions per model. In this reported comparison, DeepSeek-V4-Pro-Qwen3.5-9B achieves the highest average at 90.53%. Its compact 4B sibling is now included alongside the Qwen and Claude Mythos-distilled references.

2.3 Inference Efficiency

🛠️ 3. Generalization Beyond the Training Mix

✅ 4. Instruction Following & Output-Format Compliance

🎮 Game Showcase: Star Skip by Kyle

🎯 5. Recommended Uses

Mathematical problem solving and step-by-step derivation
Physics, chemistry, and broader STEM question answering
Structured reasoning and analytical instruction following
Research on reasoning distillation and cross-domain SFT transfer
Experimental tool-calling or programming workflows where outputs are independently validated

⚠️ 6. Limitations

This is an experimental 9B community model and remains subject to hallucinations, reasoning errors, and unstable behavior on difficult or underspecified tasks.
The reported MMLU-Pro results are based on 500 randomly sampled questions from each of the Math, Physics, and Chemistry subsets, rather than the complete subsets; they are not a comprehensive capability or safety evaluation.
GSM8K and MMLU-Pro used different inference builds and backends, so their scores should not be used to infer quantization equivalence.
Programming and tool-use gains were observed without direct coding supervision but are described only as small, preliminary generalization gains.
Users should independently verify high-stakes mathematical, scientific, or factual outputs.