Instructions to use brokencircuitranch/gemma4-hermes-tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brokencircuitranch/gemma4-hermes-tools with Transformers:

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

pipe = pipeline("image-text-to-text", model="brokencircuitranch/gemma4-hermes-tools")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("brokencircuitranch/gemma4-hermes-tools")
model = AutoModelForImageTextToText.from_pretrained("brokencircuitranch/gemma4-hermes-tools")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use brokencircuitranch/gemma4-hermes-tools with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="brokencircuitranch/gemma4-hermes-tools",
	filename="gemma4-hermes-tools-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use brokencircuitranch/gemma4-hermes-tools with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_M

Use Docker

docker model run hf.co/brokencircuitranch/gemma4-hermes-tools:Q4_K_M

LM Studio
Jan

vLLM

How to use brokencircuitranch/gemma4-hermes-tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brokencircuitranch/gemma4-hermes-tools"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brokencircuitranch/gemma4-hermes-tools",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/brokencircuitranch/gemma4-hermes-tools:Q4_K_M

SGLang

How to use brokencircuitranch/gemma4-hermes-tools 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 "brokencircuitranch/gemma4-hermes-tools" \
    --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": "brokencircuitranch/gemma4-hermes-tools",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "brokencircuitranch/gemma4-hermes-tools" \
        --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": "brokencircuitranch/gemma4-hermes-tools",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use brokencircuitranch/gemma4-hermes-tools with Ollama:
```
ollama run hf.co/brokencircuitranch/gemma4-hermes-tools:Q4_K_M
```

Unsloth Studio

How to use brokencircuitranch/gemma4-hermes-tools with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for brokencircuitranch/gemma4-hermes-tools to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for brokencircuitranch/gemma4-hermes-tools to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for brokencircuitranch/gemma4-hermes-tools to start chatting

How to use brokencircuitranch/gemma4-hermes-tools with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "brokencircuitranch/gemma4-hermes-tools:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use brokencircuitranch/gemma4-hermes-tools with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf brokencircuitranch/gemma4-hermes-tools:Q4_K_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 brokencircuitranch/gemma4-hermes-tools:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use brokencircuitranch/gemma4-hermes-tools with Docker Model Runner:
```
docker model run hf.co/brokencircuitranch/gemma4-hermes-tools:Q4_K_M
```

Lemonade

How to use brokencircuitranch/gemma4-hermes-tools with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull brokencircuitranch/gemma4-hermes-tools:Q4_K_M

Run and chat with the model

lemonade run user.gemma4-hermes-tools-Q4_K_M

List all available models

lemonade list

gemma4-hermes-tools / README.md

brokencircuitranch

Update README.md

66cf1ce verified about 1 month ago

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	---
	license: gemma
	language:
	- en
	base_model:
	- google/gemma-4-26B-A4B-it
	library_name: transformers
	tags:
	- unsloth
	- gemma
	- tool-use
	- function-calling
	- qlora
	---


	Fine-tuned version of [google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it) for reliable tool use and function calling.

	## Training

	- Base model: google/gemma-4-26B-A4B-it (Mixture of Experts)
	- Fine-tuning framework: [Unsloth](https://github.com/unslothai/unsloth)
	- Hardware: NVIDIA A100 80GB (HuggingFace Space)
	- Method: QLoRA (4-bit) → merged to 16-bit

	## Training Data

	- [NousResearch/hermes-function-calling-v1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1) — 1,893 examples of
	structured tool use and function calling in Hermes format
	- [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) — 5,000 sampled examples for general instruction following and
	reasoning

	Total: 6,893 examples, 2 epochs

	## Training Results

	\| Step \| Loss \|
	\|------\|------\|
	\| 10 \| 1.825 \|
	\| 50 \| 0.374 \|
	\| 200 \| 0.196 \|
	\| 500 \| 0.110 \|
	\| 862 \| 0.113 \|

	Final training loss: 0.224

	## Intended Use

	Designed for agentic pipelines requiring reliable structured tool call generation. Tested with Ollama for local inference.

	## Files

	- `model-0000x-of-00002.safetensors` — merged 16-bit weights
	- `gemma4-hermes-tools-Q4_K_M.gguf` — quantized for local inference via Ollama/llama.cpp

	## License

	Inherits [Gemma Terms of Use](https://ai.google.dev/gemma/terms)