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 Chungulus/Agents-A1-Q5_K_M-imatrix-gguf-fable5-calibrated:Q5_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 "Chungulus/Agents-A1-Q5_K_M-imatrix-gguf-fable5-calibrated:Q5_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

A1-Q5_K_M-imatrix

Static imatrix-calibrated GGUF quant of InternScience/Agents-A1.

llama-cli -hf Chungulus/Agents-A1-Q5_K_M-imatrix-gguf-fable5-calibrated:Agents-A1-Q5_K_M-imatrix-gguf-fable5-calibrated.gguf -p "Write a Python sorting function" -n 160

File

File Size SHA-256
Agents-A1-Q5_K_M-imatrix-gguf-fable5-calibrated.gguf 23.03 GiB 1e95ead178c1cfc187fcac4627515644c0105d3e5ec08f3f2fb9e317470b7840

Quality Snapshot

F16 baseline mini accuracy: 89.58%. F16 baseline PPL on KL holdout: 13.0194.

Metric Value
Mini accuracy 89.58%
Retention vs F16 100.00%
Mean KLD vs F16 0.007785
Same top p 96.83%

Notes

  • Calibration source: Glint-Research/Fable-5-traces
  • MTP is not included because the downloaded checkpoint did not contain MTP tensors.
  • Static imatrix GGUF, not Unsloth Dynamic 2.0 / UD2.
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