How to use from
OpenClaw
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aimi-models/llm:Q4_K_M
Configure OpenClaw
# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "aimi-models/llm:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Quick Links

LLM Mirror (A.I.M.I)

Mirror of A.I.M.I's default text-LLM GGUFs, re-hosted for stable URLs. Contents unmodified from upstream unsloth/Qwen quantizations.

Used by A.I.M.I's chat engine via llama.cpp. Qwen3-8B is the 16 GB tier default; Mistral Small 3.2 24B is the 24 GB+ tier upgrade.

Files

File Upstream Size Tier
Qwen3-8B-Q4_K_M.gguf Qwen/Qwen3-8B-GGUF ~5.0 GB 16 GB default
Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF ~14.3 GB 24 GB+ default

Total: ~19 GB.

License

Both models Apache 2.0:

  • Mistral Small 3.2 24B Instruct: Apache 2.0 from Mistral AI. Unsloth's GGUF re-quantization inherits Apache 2.0.
  • Qwen3-8B: Apache 2.0 from Alibaba Cloud / Qwen team. GGUF by Qwen team directly.

Redistributed unchanged.

Attribution

  • Mistral Small 3.2: Mistral AI (2025). Base Apache 2.0 release.
  • Qwen3-8B: Alibaba Cloud / Qwen team (2025). Base Apache 2.0 release.
  • GGUF conversions: unsloth (Mistral), Qwen team (Qwen3).
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GGUF
Model size
24B params
Architecture
llama
Hardware compatibility
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