How to use from
Hermes Agent
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Chungulus/Agents-A1-Q6_K-imatrix-gguf-fable5-calibrated:Q6_K
Configure Hermes
# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Chungulus/Agents-A1-Q6_K-imatrix-gguf-fable5-calibrated:Q6_K
Run Hermes
hermes
Quick Links

A1-Q6_K-imatrix

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

llama-cli -hf Chungulus/Agents-A1-Q6_K-imatrix-gguf-fable5-calibrated:Agents-A1-Q6_K-imatrix-gguf-fable5-calibrated.gguf -p "Write a Python sorting function" -n 160

File

File Size SHA-256
Agents-A1-Q6_K-imatrix-gguf-fable5-calibrated.gguf 26.56 GiB 4ddb5fc1a537d364391a4adc4172d364ec31782e6b1e4d802e8fbe7b02e99ee9

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 0.007926
Same top p 94.44%

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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