🜂 Atanor-4B

A fine-tune of Qwen3.5-4B specialized for agentic tool-use inside Hermes Agent.

There were 9B and 27B versions of Qwen fine-tuned to run as agents in Hermes (the Carnice models). That made me wonder: could a smaller model do the same job?

Atanor-4B is my answer — and my first fine-tune ever. It was trained entirely locally, on a single RTX 3090.

The name Atanor is the alchemist's furnace: the 4B is the lead that goes in, the agent is what comes out. 🜂

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Results — agentic evaluation

Measured on a 60-task Hermes-native agent benchmark (real tool execution inside Hermes Agent, deterministic / temperature 0), base vs fine-tune:

Metric	Qwen3.5-4B (base)	Atanor-4B
Agent score	0.81	0.84
Picking the right tool	30%	60% ⬆️ doubled
Task success	67%	73%

The core agent skill — choosing the correct tool for a task — doubled (30% → 60%).

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How it was made

Following the Carnice recipe, in two LoRA stages (BF16, on one RTX 3090):

• Stage A — reasoning repair: Bespoke-Stratos + NuminaMath-CoT
• Stage B — Hermes tool-use: the kai-os/carnice-glm5-hermes-traces traces (the full set, seq len 16384)

~33 hours of training, zero crashes.

Files in this repo

File	What it is
atanor-4b-full-Q4_K_M.gguf	Quantized (~2.6 GB) — run it directly in llama.cpp / Hermes / Ollama
atanor-4b-full-Q4_K_M-f16.gguf	Full-precision GGUF (~8 GB) — for re-quantizing or lossless inference
*.safetensors (merged)	Full merged model for transformers / further fine-tuning
adapter/	The LoRA adapter alone, to apply on the base model

Usage (llama.cpp / Hermes)

# llama.cpp server
llama-server -m atanor-4b-full-Q4_K_M.gguf --jinja -ngl 99 -c 32768 --alias atanor

# point a Hermes profile at it (provider base_url: http://localhost:8081/v1)
hermes chat --profile atanor -q "read data.csv and total the 'south' region using the terminal"

Thinking is on by default — it helps the model reason about which tool to use. (Pass chat_template_kwargs: {"enable_thinking": false} to disable.)

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This is my first fine-tune, and version one. More to come. I learned a ton — and the best part is it was all done at home, on my own GPU.

Built with the Hermes Agent ecosystem. Base model © Qwen, Apache 2.0.