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CodeClawd — Qwen3.5-9B Claude Code (GGUF)

Name: Qwen3.5-9B-Claude-Code-GGUF
Availability: InStock

GGUF quantizations of empero-ai/Qwen3.5-9B-Claude-Code — A supervised fine-tune of empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill on real Claude Code / Codex agentic sessions from the DataClaw dataset collection.

The model is trained to behave like a capable coding agent: thinking through problems, calling tools, interpreting results, and responding clearly — mirroring the full agentic loop of a real Claude Code session.

The model thinks with <think> tags, calls tools with XML-style <tool_call> blocks.

Available Quantizations

Filename	Quant	Size	RAM Required	Quality
`Qwen3.5-9B-Claude-Code-Q2_K.gguf`	Q2_K	~3.6 GB	~6 GB	Lowest — not recommended
`Qwen3.5-9B-Claude-Code-Q3_K_M.gguf`	Q3_K_M	~4.5 GB	~7 GB	Low
`Qwen3.5-9B-Claude-Code-Q4_0.gguf`	Q4_0	~5.2 GB	~8 GB	Acceptable
`Qwen3.5-9B-Claude-Code-Q4_K_M.gguf`	Q4_K_M	~5.4 GB	~8 GB	Recommended — best size/quality tradeoff
`Qwen3.5-9B-Claude-Code-Q5_K_M.gguf`	Q5_K_M	~6.4 GB	~9 GB	High
`Qwen3.5-9B-Claude-Code-Q6_K.gguf`	Q6_K	~7.3 GB	~10 GB	Very high
`Qwen3.5-9B-Claude-Code-Q8_0.gguf`	Q8_0	~9.5 GB	~12 GB	Near-lossless
`Qwen3.5-9B-Claude-Code-F16.gguf`	F16	~18 GB	~22 GB	Lossless

Recommended: Q4_K_M for most users. Q5_K_M or Q6_K if you have extra VRAM and want higher quality reasoning.

Model Details

Property	Value
Original model	`empero-ai/Qwen3.5-9B-Claude-Code`
Base model	`empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill`
Fine-tune type	SFT (LoRA r=64/alpha=128, merged)
Parameters	9B
Training data	DataClaw — 29 datasets of real Claude Code / Codex sessions
Context length	4096 tokens
License	Apache 2.0

Usage with llama.cpp

bash

# Basic inference
./llama-cli \
  -m Qwen3.5-9B-Claude-Code-Q4_K_M.gguf \
  -n 1024 \
  --temp 0.7 \
  --top-p 0.9 \
  -p "<|im_start|>system
You are an expert AI coding assistant. You help users with software engineering tasks including writing code, debugging, refactoring, explaining code, and more. You think through problems carefully inside <think> tags before acting. You have access to tools like Read, Edit, Write, Bash, Grep, Glob, and others to interact with the user's codebase. You use tools when needed and provide clear, concise responses.<|im_end|>
<|im_start|>user
Read the file main.py and explain what it does.<|im_end|>
<|im_start|>assistant
"

bash

# As an OpenAI-compatible server
./llama-server \
  -m Qwen3.5-9B-Claude-Code-Q4_K_M.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  -c 4096 \
  -ngl 99

Usage with Ollama

Create a Modelfile:

FROM Qwen3.5-9B-Claude-Code-Q4_K_M.gguf

SYSTEM """You are an expert AI coding assistant. You help users with software engineering tasks including writing code, debugging, refactoring, explaining code, and more. You think through problems carefully inside <think> tags before acting. You have access to tools like Read, Edit, Write, Bash, Grep, Glob, and others to interact with the user's codebase. You use tools when needed and provide clear, concise responses. You show your reasoning process and use tools methodically to accomplish tasks."""

PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER num_ctx 4096

bash

ollama create codeclawd -f Modelfile
ollama run codeclawd

Usage with LM Studio

Download the .gguf file of your chosen quantization
Open LM Studio → Load Model → select the file
In the system prompt field, paste:

You are an expert AI coding assistant. You help users with software engineering tasks including writing code, debugging, refactoring, explaining code, and more. You think through problems carefully inside <think> tags before acting. You have access to tools like Read, Edit, Write, Bash, Grep, Glob, and others to interact with the user's codebase. You use tools when needed and provide clear, concise responses. You show your reasoning process and use tools methodically to accomplish tasks.

Set temperature: 0.7, top-p: 0.9, context: 4096

Agentic Format

This model is trained on the full agentic loop:

user → <think>...</think> → <tool_call> → <tool_result> → response → repeat

Tool Call Format

xml

<tool_call>
<tool>Read</tool>
<input>
{"file_path": "/path/to/file.py"}
</input>
</tool_call>

<tool_result>
<tool>Read</tool>
<output>
... file contents ...
</output>
</tool_result>

Thinking Format

xml

<think>
The user wants to refactor the auth module. I should first read the existing
implementation before suggesting changes.
</think>

Intended Use

Local agentic coding assistants on consumer hardware
IDE integrations (Continue, Cursor, etc.) running locally
Offline / air-gapped development environments
Research into agentic SFT distillation from real sessions

Limitations

Tool format is XML-based — not OpenAI function-call JSON
SFT only (no RLHF or DPO)
May hallucinate tool outputs if not connected to a real tool executor
Quantization below Q4 may degrade reasoning quality noticeably

Original Model

HF (safetensors): empero-ai/Qwen3.5-9B-Claude-Code

License

Apache 2.0 — see base model for details.

empero-ai/Qwen3.5-9B-Claude-Code-GGUF

CodeClawd — Qwen3.5-9B Claude Code (GGUF)

Available Quantizations

Model Details

Usage with llama.cpp

Usage with Ollama

Usage with LM Studio

Agentic Format

Tool Call Format

Thinking Format

Intended Use

Limitations

Original Model

License

empero-ai/
Qwen3.5-9B-Claude-Code-GGUF