jorge-erdb/Qwen3.5-9B-DeepSeek-V4-Flash-4bit-GGUF

Qwen3.5-9B-DeepSeek-V4-Flash-GGUF

GGUF quantizations of Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash, Jackrong's distillation of DeepSeek-V4's reasoning into the Qwen3.5-9B architecture using the DeepSeek-V4-Distill-8000x dataset.

Quantization Details

Dynamic IQ4_NL Recipe

The D-IQ4_NL variant uses a custom tensor-level precision recipe rather than a uniform IQ4_NL quantization.

Download

pip install -U "huggingface_hub[cli]"

# Q4_K_M (smallest, broadest backend support)
huggingface-cli download jorge-erdb/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF --include "*Q4_K_M.gguf" --local-dir ./

# D-IQ4_NL (dynamic, Q6_K-protected attention/SSM)
huggingface-cli download jorge-erdb/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF --include "*D-IQ4_NL.gguf" --local-dir ./

[!IMPORTANT] ## Apple Metal Backend Warning D-IQ4_NL uses non-linear quantization on FFN tensors. It performs sub-optimally on Apple's Metal backend due to the lack of native support for non-linear dequantization kernels. If you are running on an Apple Silicon Mac with GPU offloading via Metal, you will likely experience: - Slower inference compared to linear quants of similar size (e.g., Q4KM) - No speed benefit from the ARM weight repacking that IQ4NL supports on CPU **If you're on Apple Metal, use the Q4KM variant instead.** D-IQ4NL is best suited for CUDA (NVIDIA GPU) or CPU-only inference.

Credits

• —Quantization: jorge-erdb
• —Distillation & training: Jackrong — Qwen3.5-9B-DeepSeek-V4-Flash
• — ---

🌟 Qwen3.5-9B-DeepSeek-V4-Flash

💡 Model Overview & Design

[!NOTE] Qwen3.5-9B-DeepSeek-V4-Flash is an efficient reasoning model distilled using high-quality data from DeepSeek-V4.

• —By leveraging the dataset Jackrong/DeepSeek-V4-Distill-8000x, this model successfully transfers the advanced structured reasoning and multi-step problem-solving capabilities of the DeepSeek-V4 architecture into the highly efficient Qwen3.5-9B parameter space.

• —This model was trained in an Unsloth environment, prioritizing stable gradient propagation and rigorous data curation to ensure the distillation process avoids merely learning "hollow chain-of-thought" and instead captures genuine logical generalization.

Designed for:

• —🧩 Structured Reasoning: Inheriting DeepSeek-V4's deep logic capabilities.
• —⚡ Flash Inference: Maintaining the token-efficiency and speed of the 9B parameter size.
• —🔧 Tool-augmented Workflows: Reliable agentic action generation.

🍎 About the Teacher Model: DeepSeek-V4

[DeepSeek-V4](https://huggingface.co/collections/deepseek-ai/deepseek-v4) is the latest flagship open-source model series from DeepSeek, engineered for extreme efficiency, million-token long context (1M), and advanced Agentic workflows. As the source for this distillation, DeepSeek-V4 provides the high-fidelity reasoning signals necessary to push a 9B model beyond its architectural limits.

Key Technical Strengths of the Teacher Model:

• —🏆 World-Class Reasoning & Coding: DeepSeek-V4 demonstrates elite performance in mathematics (MATH-500), STEM subjects, and real-world software engineering (SWE-bench). Its "Think" modes provide the sophisticated Long-CoT (Chain-of-Thought) traces that define this model's logic.
• —🧠 Architectural Innovation: Hybrid Attention & DSA:* Features Token-level compression and DeepSeek Sparse Attention, which reduces KV Cache memory overhead by up to 90%, allowing for highly efficient long-context processing.
• —Engram Memory & mHC: Utilizes Manifold-constrained Hyper-connections to decouple factual knowledge retrieval from dynamic logical reasoning, ensuring exceptional stability and generalization.
• —🤖 Agent-Centric Design: Specifically optimized for multi-step tool calling and complex environment interaction, ensuring that the distilled knowledge includes reliable "how-to-act" procedures, not just "how-to-talk."

By distilling from DeepSeek-V4-Flash, we have successfully mapped the high-density logic of a trillion-parameter class model onto the agile and high-speed Qwen3.5-9B framework.

🤝 Collaboration & Training Details

This model is the result of a close collaboration with hardware engineer Kyle Hessling. He generously provided the crucial compute equipment and managed both the rigorous post-training testing and continuous server maintenance. I want to express my gratitude to Kyle for his invaluable support! You can find him on X/Twitter here: @KyleHessling1

Training Infrastructure & Configuration:

• —🖥️ Hardware: NVIDIA DGX
• —💾 Training Data: DeepSeek-V4-Distill-8000x
• —🧪 Training Method: Distillation

🎯 Motivation & Distillation Insights

• —🧠 Latent Knowledge Activation: DeepSeek-V4's reasoning traces help the Qwen3.5-9B model activate its existing latent knowledge more effectively.
• —🏗️ Learning Procedures: The model learns actual problem-solving procedures, not just the output format.
• —🚀 Efficiency: The 8000x dataset provides a dense signal, allowing the 9B model to converge on reasoning tasks much faster than traditional large-scale SFT.

📊 Evaluation

[!IMPORTANT] This is an early controlled Q5_K_M comparison between Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash and the official Qwen3.5-9B base model. This evaluation was completed by Kyle Hessling, who ran the same evaluation suite twice under the same local inference conditions: once on the DeepSeek-V4 distill model and once on the official Qwen3.5-9B base model.

• —❤️ Special thanks to Kyle for the careful post-training testing and detailed comparison report. You can find him on X/Twitter here: [@KyleHessling1](https://x.com/KyleHessling1).
• —📄 Full evaluation report: [KyleHessling1/jackrong-deepseek-9b-eval](https://huggingface.co/spaces/KyleHessling1/jackrong-deepseek-9b-eval).

🔬 Supporting Evidence

Recent work and empirical tests support this distillation approach:

*Ren et al., 2026 — Rethinking Generalization in Reasoning SFT*** (arXiv:2604.06628)

The paper suggests that generalization in reasoning SFT is conditional. Key takeaways:

• —High-quality long-CoT data from DeepSeek-V4 enables cross-domain transfer.
• —Optimization Discipline: Short, highly-curated distillation (8000 examples) prevents the model from overfitting to the teacher's stylistic quirks while preserving the core reasoning engine.

🛠️ Best Practices

For optimal performance, we recommend the following generation parameters:

• —temperature=0.7 to 1.0 (Use lower temperature for strict coding tasks, higher for creative reasoning)
• —top_p=0.95

When interacting with the model, using a structured prompt template or standard ChatML format will yield the best reasoning results.

📚 Resources & Guides

👉 [GitHub Repository: Jackrong-llm-finetuning-guide](https://github.com/R6410418/Jackrong-llm-finetuning-guide.git) Visit the repository to dive into the codebase and reproduce the results locally or on Colab.

📥 Core Technical Document

🔗 [Complete Fine-Tuning Guide (PDF)](https://github.com/R6410418/Jackrong-llm-finetuning-guide/blob/main/guidePDF/Qwopus3-5-9b-Colab_complete_guide_to_llm_finetuning.pdf)

A Note: My goal isn't just to detail a workflow, but to demystify LLM training. Beyond the social media hype, fine-tuning isn't an unattainable ritual—often, all you need is a Google account, a standard laptop, and relentless curiosity. All training and testing for this project were self-funded. If you find this model or guide helpful, a Star ⭐️ on GitHub would be the greatest encouragement. Thank you! 🙏

⚠️ Limitations

• —Parameter Constraints: While enhanced by DeepSeek-V4 distillation, the model is still bound by the 9B parameter limits and may struggle with extremely obscure knowledge.
• —Over-reasoning: On very simple queries, the model might still attempt to produce a lengthy reasoning chain due to the SFT bias.
• —Safety Trade-offs: Asymmetric gains mean that while reasoning improves, certain alignment-sensitive behaviors might regress.

🙏 Acknowledgements

Special thanks to:

• —DeepSeek Team for the foundational advancements in the V4 architecture.
• —Unsloth for efficient fine-tuning frameworks.
• —Open-source datasets and community contributors.
• —Researchers exploring reasoning SFT and distillation.

📖 Citation

@misc{jackrong_qwen35_9b_deepseek_v4_flash,
  title        = {Qwen3.5-9B-DeepSeek-V4-Flash},
  author       = {Jackrong},
  year         = {2026},
  publisher    = {Hugging Face}
}