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🔬 DeepSeek-V4-Pro Distillation 🧠 4B Parameters 📐 ~250K Math & STEM Samples ⚡ MTP-Q8 Evaluated 💡 What is DeepSeek-V4-Pro-Qwen3.5-4B? DeepSeek-V4-Pro-Qwen3.5-4B is a reasoning-focused fine-tune of Qwen3.5-4B , distilled from responses generated by DeepSeek-V4-Pro in Max Effect mode . It follows the same training pipeline and approximately 250,000-sample mathematics and STEM mixture used for the 9B release, while targeting a substantially smaller and more accessible deployment class. 🧩 Structured Reasoning Learns multi-step solution patterns and explicit problem decomposition from a strong teacher.

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🔬 DeepSeek-V4-Pro Distillation 🧠 9B Parameters 📐 ~250K Math & STEM Samples ⚡ MTP & NVFP4 Evaluated 💡 What is DeepSeek-V4-Pro-Qwen3.5-9B? DeepSeek-V4-Pro-Qwen3.5-9B is a reasoning-focused fine-tune of Qwen3.5-9B , distilled from responses generated by DeepSeek-V4-Pro in Max Effect mode . The release concentrates its supervised signal on mathematics and STEM problem solving while preserving the efficiency and practical deployability of the 9B parameter class. 🧩 Structured Reasoning Learns rigorous problem decomposition and multi-step solution patterns from a strong teacher.

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This model Jackrong/MLX-Qwopus3.5-9B-v3-4bit was converted to MLX format from Jackrong/Qwopus3.5-9B-v3 using mlx-lm version **0.30.7**.

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**Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled** is a highly capable reasoning model fine-tuned on top of the Qwen3.5-0.8B dense architecture. The model's core directive is to leverage state-of-the-art Chain-of-Thought (CoT) distillation primarily sourced from Claude-4.6 Opus interactions. Through Supervised Fine-Tuning (SFT) focusing specifically on structured reasoning logic, this model excels in breaking down complex user problems, planning step-by-step methodologies within strictly formatted ` ` tags, and ultimately delivering precise, nuanced solutions. The model includes targeted optimizations addressing Qwen3.5’s tendency toward excessive transitional or repetitive reasoning on simple queries. Through deep distillation and structural imitation of Claude-4.6-Opus reasoning chains, the model adopts a more efficient structured thinking pattern: **“Let me analyze this request carefully: 1..2..3...”.** This streamlined reasoning paradigm significantly reduces redundant cognitive loops while preserving deep analytical capacity, resulting in substantially improved inference efficiency.

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🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab. **🔗 Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)** * **The Full Pipeline:** A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face. * **Beginner Friendly:** Includes an introductory guide to getting started with Google Colab and Unsloth. * *Feedback welcome! If you spot any areas for improvement, please let me know and I will update it promptly.*

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

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选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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