gpt-oss-20b-hermes_agent-tool-finetune_gguf

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gpt-oss-20b · Hermes-Agent tool finetune · GGUF

GGUF quants for llama.cpp, Ollama, and LM Studio. Five quants shipped — pick by RAM budget.

• Format — GGUF
• Quants shipped — Q3_K_M, Q4_K_M, Q5_K_M, Q8_0, F16
• Recommended — Q4_K_M for 16 GB RAM, Q8_0 for quality
• Runtime — llama.cpp, Ollama, LM Studio, koboldcpp

What this is

A tool-use finetune of OpenAI's gpt-oss-20b for Hermes-Agent, a local agent framework that needs models which call tools reliably, follow multi-turn instructions, and don't argue with system prompts.

The base model is the 21B-parameter (3.6B active) Mixture-of-Experts release from OpenAI. This finetune preserves the Harmony chat template and the reasoning-effort knob, and improves:

• Function-calling adherence (correct JSON, no commentary mid-call)
• Long agent loops (10+ turns of tool → observe → plan)
• System-prompt fidelity (respects role boundaries and refusal/allow-list rules)

It is not affiliated with NousResearch's Hermes model series. "Hermes-Agent" here refers to the local agent framework only.

Files

(Sizes are approximate; check the file list for exact bytes.)

Quickstart

llama.cpp

./llama-server \
  -hf fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_gguf:Q4_K_M \
  --port 1234 \
  -c 8192 \
  --jinja

Ollama

ollama run hf.co/fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_gguf:Q4_K_M

LM Studio

Search for fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_gguf in the Discover tab and pick a quant. Enable "Use Jinja chat template" in the model settings so Harmony renders correctly.

Hermes-Agent integration

Add a profile in ~/.hermes/config.yaml:

profiles:
  gpt-oss-20b-tools:
    provider: openai
    base_url: http://127.0.0.1:1234/v1   # LM Studio / vLLM / mlx_lm.server
    model: fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_gguf
    temperature: 0.7
    top_p: 0.95
    min_p: 0.1                            # important for MoE stability
    max_tokens: 8192
    tool_choice: auto

Then hermes profile use gpt-oss-20b-tools and the agent loop will route tool calls through this model.

Sampling

Harmony reasoning effort: set the system message to Reasoning: low|medium|high. high is roughly 3-4x more output tokens but noticeably better on multi-step tool plans.

Training

• Base: openai/gpt-oss-20b
• Method: LoRA SFT (rank 64, alpha 16) merged back into BF16
• Frame: Unsloth + TRL on a single H100 (80 GB)
• Data: ~42k tool-use traces from Hermes-Agent sessions, filtered for successful tool calls and clean JSON. No synthetic distillation.
• Length: 8192 tokens, packing on
• Loss: assistant-only, mask user/system/tool

The _16bit repo holds the merged BF16 weights. The _4bit, _mlx, and _gguf repos are quantizations of that checkpoint.

Limitations

• Math and code-generation are unchanged from the base — this finetune optimizes the agent loop, not raw reasoning.
• The model can over-call tools when given vague instructions. Add a "if you can answer directly, do so" line to the system prompt.
• English only. Other languages were not in the training mix.
• Not safety-tuned beyond what gpt-oss-20b already provides.

Other formats

• BF16 reference — full precision, vLLM / Transformers
• MXFP4 4-bit — fits a 16 GB GPU
• MLX — Apple Silicon native
• GGUF — llama.cpp / Ollama / LM Studio

License

Apache-2.0, inherited from the base model. No additional restrictions.

Citation

@misc{fesalfayed_gptoss20b_hermesagent_2025,
  author = {Fayed, Fesal},
  title  = {gpt-oss-20b Hermes-Agent tool finetune (gguf)},
  year   = {2025},
  url    = {https://huggingface.co/fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_gguf},
}