TheCidSama/Qwen3.5-9b-Claude-4.8-Opus-reasoning

⚡ Qwen3.5-9B-Claude-4.8 (GGUF) — ✨

🧠 More accuracy, 20% less rambling — a sharp local model for code, math & general tasks

Runs on modest hardware. With ~8-9 GB of VRAM or unified memory free, you get a private, offline reasoning assistant that thinks less and lands more. 🚀 This is the v1 edition — tuned to reason efficiently, cut redundant chain-of-thought, and still hit the correct final answer. All local, all yours. 💚

🎯 What it is

A focused fine-tune of Qwen3.5-9B with trace-inversed CoT from Opus4.8 (Dataset not published anywhere) specialized for coding, mathematics, and cybersecurity reasoning. The headline trait: it reaches the goal with roughly 20% shorter thinking traces than the base model while maintaining final-answer accuracy — less wandering, faster tokens-to-solution, lower latency. 🧠⚡

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✨ Highlights

• 🪶 ~20% shorter reasoning vs. base model, with accuracy held — tighter CoT, faster answers.
• 💻 Improved coding — cleaner, more runnable solutions across common languages.
• 🧮 Strong math — multi-step problems with the reasoning shown, then a clear final answer.
• 🛡️ Security-aware — geared toward defensive concepts, code review, and CTF-style learning.
• 📦 One quant, well-tuned: ships as Q4_K_M — the size/quality sweet spot.

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📦 Download (GGUF quant)

Quant	Size	Vibe
🔵 Q4_K_M	~5.6 GB	the sweet spot 👌 (recommended — balanced quality & footprint)

💡 Only Q4_K_M is published for now. Want another quant (Q5_K_M, Q8_0, f16)? Open a discussion and I'll consider it.

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🧮 "Will it fit?" — context cheat-sheet

Rough estimates 🤓 (assumes q8_0 KV cache + ~1.5 GB overhead; switch to q4_0 KV cache for ≈2× more context).

Your VRAM / unified mem	🔵 Q4_K_M (~8-9G)
8 GB	~24K ctx
12 GB	~64K
16 GB	~100K
24 GB	comfortable headroom

💡 Apple Silicon / integrated GPUs with unified memory work too — same idea, just slower than a dGPU.

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🚀 How to run it

Option A — llama.cpp (recommended) 🦙

1. Download …-Q4_K_M.gguf and llama-server from llama.cpp.

   ⚠️ Use a recent llama.cpp build for current Qwen3 architectures.

2. Run a server:

```bash llama-server
-m ./qwen3.5-9b-reasoner-Q4_K_M.gguf
--ctx-size 16384
--n-gpu-layers 99
-fa on
--cache-type-k q8_0 --cache-type-v q8_0
--temp 0.7 --top-p 0.8 --top-k 20
--host 0.0.0.0 --port 8080 ```

3. Connect to your agent and chat. 🎉

Option B — one-click apps 🖱️

Works in LM Studio, Jan, Ollama, etc. — import the GGUF, pick the quant, go. 🐾

🧠 Thinking mode

This model reasons before answering. Keep thinking enabled (the default chat template handles it). Suggested sampling: temp 0.7, top_p 0.95, top_k 20. For deterministic code/math, try greedy (temp 0).

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⚠️ Good to know

• Not safety-aligned for production. This is a specialized reasoning fine-tune — add your own guardrails, input/output filtering, and review before any production or user-facing deployment. Use responsibly. 🙏 [Not uncensored]
• Strongest in code, math, and security reasoning; double-check general-knowledge facts and figures.
• English-centric.

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🙏 Acknowledgements

Special thanks to:

• The Qwen team for the strong Qwen3.5 base model.
• Unsloth for efficient fine-tuning frameworks.

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📚 Base & License

• License: Apache 2.0
• Base model: Qwen/Qwen3.5-9B