Instructions to use fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit")
model = AutoModelForCausalLM.from_pretrained("fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit

SGLang

How to use fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit with Docker Model Runner:
```
docker model run hf.co/fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit
```

gpt-oss-20b · Hermes-Agent tool finetune · BF16

Reference checkpoint. Full BF16 weights, ready for vLLM, Transformers, or further quantization.

Format — Safetensors, BF16
Size on disk — ~41 GB
VRAM (inference) — ~42 GB
Recommended runtime — vLLM ≥ 0.6, Transformers ≥ 4.45

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.

Quickstart

vLLM

pip install -U vllm
vllm serve fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.92

OpenAI-compatible endpoint at http://127.0.0.1:8000/v1.

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit")
model = AutoModelForCausalLM.from_pretrained(
    "fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

msgs = [
    {"role": "system", "content": "You are a helpful agent. Reasoning: low"},
    {"role": "user", "content": "What is 17 * 23?"},
]
inputs = tok.apply_chat_template(msgs, return_tensors="pt", add_generation_prompt=True).to(model.device)
out = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.95)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

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_16bit
    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

Param	Value	Why
temperature	0.7	balanced; drop to 0.2 for strict tool calls
top_p	0.95	standard nucleus
min_p	0.1	required for MoE — prevents dead-expert tokens
repetition_penalty	1.0	the model handles repetition itself

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 (16bit)},
  year   = {2025},
  url    = {https://huggingface.co/fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit},
}

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Safetensors

Model size

21B params

Tensor type

F16

Model tree for fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit

Base model

openai/gpt-oss-20b

Finetuned

(515)

this model

Collection including fesalfayed/gpt-oss-20b-hermes_agent-tool-finetune_16bit

finetuned_hermes-function-calling-v1

Collection

Models fine-tuned on Hermes tool calling dataset • 4 items • Updated 30 days ago • 1