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-IQ3_M-imatrix-gguf-fable5-calibrated:IQ3_M
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-IQ3_M-imatrix-gguf-fable5-calibrated:IQ3_M
Run Hermes
hermes
Quick Links

Agents-A1-IQ3_M-imatrix-gguf-fable5-calibrated

Fable-5 trace calibrated imatrix GGUF quant of InternScience/Agents-A1.

File

File Size SHA-256
Agents-A1-IQ3_M-imatrix-gguf-fable5-calibrated.gguf 14.38 GiB d9b3a209f0e72f2d4ba34507e6012004525cc12ba7b86fca1639e35c7470fa61

Quick Start

llama-cli -hf Chungulus/Agents-A1-IQ3_M-imatrix-gguf-fable5-calibrated:Agents-A1-IQ3_M-imatrix-gguf-fable5-calibrated.gguf -p "Write a Python sorting function" -n 160

Ollama

ollama create agents-a1-iq3_m-imatrix -f Modelfile
ollama run agents-a1-iq3_m-imatrix

Which File Should I Download?

Use case Recommendation
Recommended hardware 8-12 GB RAM
Best for tiny but usable

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 0.045312
Same top p 92.06%

Notes

  • Calibration source: Glint-Research/Fable-5-traces
  • Calibration source revision: e05c417852fc59fd8da758e68b352732423ca0cb
  • GGUF quantization method: llama.cpp with imatrix calibration.
  • Static imatrix GGUF; not Unsloth Dynamic 2.0 / UD2.
  • MTP is not included because the downloaded checkpoint did not contain MTP tensors.
  • This repo contains local quantization artifacts only.
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