DeepSeek V4 Pro Qwen3.5 9B UNCENSORED Heretic
DeepSeek V4 Pro Qwen3.5 9B UNCENSORED Heretic by xtools-at, a text-generation model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.
Comparison
| Feature | DeepSeek V4 Pro Qwen3.5 9B UNCENSORED Heretic | Interfaze |
|---|---|---|
| Input Modalities | text, image | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | 6 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 262.1K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |
Scaling
| Feature | DeepSeek V4 Pro Qwen3.5 9B UNCENSORED Heretic | Interfaze |
|---|---|---|
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |
View model card on Hugging Face
Arbitrary-Rank Ablation (ARA) parameters
| Parameter | Value |
|---|---|
| start_layer_index | 9 |
| end_layer_index | 24 |
| preserve_good_behavior_weight | 0.8264 |
| steer_bad_behavior_weight | 0.0003 |
| overcorrect_relative_weight | 0.9645 |
| neighbor_count | 12 |
Performance
| Metric | This model | Original model (Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B) |
|---|---|---|
| Refusals | 3/100 | 100/100 |
| KL divergence | 0.0422 | 0 (by definition) |
🤝 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.