Instructions to use AmarettoLabs/Amaretto-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AmarettoLabs/Amaretto-8B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AmarettoLabs/Amaretto-8B",
	filename="amaretto-8b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AmarettoLabs/Amaretto-8B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AmarettoLabs/Amaretto-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AmarettoLabs/Amaretto-8B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AmarettoLabs/Amaretto-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AmarettoLabs/Amaretto-8B: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 AmarettoLabs/Amaretto-8B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AmarettoLabs/Amaretto-8B: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 AmarettoLabs/Amaretto-8B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AmarettoLabs/Amaretto-8B:Q4_K_M

Use Docker

docker model run hf.co/AmarettoLabs/Amaretto-8B:Q4_K_M

LM Studio
Jan

vLLM

How to use AmarettoLabs/Amaretto-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AmarettoLabs/Amaretto-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AmarettoLabs/Amaretto-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AmarettoLabs/Amaretto-8B:Q4_K_M

Ollama
How to use AmarettoLabs/Amaretto-8B with Ollama:
```
ollama run hf.co/AmarettoLabs/Amaretto-8B:Q4_K_M
```

Unsloth Studio

How to use AmarettoLabs/Amaretto-8B 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 AmarettoLabs/Amaretto-8B 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 AmarettoLabs/Amaretto-8B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AmarettoLabs/Amaretto-8B to start chatting

How to use AmarettoLabs/Amaretto-8B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AmarettoLabs/Amaretto-8B: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": "AmarettoLabs/Amaretto-8B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AmarettoLabs/Amaretto-8B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AmarettoLabs/Amaretto-8B: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 AmarettoLabs/Amaretto-8B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AmarettoLabs/Amaretto-8B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AmarettoLabs/Amaretto-8B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "AmarettoLabs/Amaretto-8B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use AmarettoLabs/Amaretto-8B with Docker Model Runner:
```
docker model run hf.co/AmarettoLabs/Amaretto-8B:Q4_K_M
```

Lemonade

How to use AmarettoLabs/Amaretto-8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AmarettoLabs/Amaretto-8B:Q4_K_M

Run and chat with the model

lemonade run user.Amaretto-8B-Q4_K_M

List all available models

lemonade list

Amaretto 8B

Amaretto 8B is a fine-tuned language model by AmarettoLabs, built for literary writing and writing assistance. It is fine-tuned from Ministral 3 8B Instruct using QLoRA.

The Amaretto line is designed to run on everyday hardware. Amaretto 8B is the mid-size model in the family — stronger creative writing quality than Amaretto 3B, while still practical for local and edge deployment.

Website: amaretto.ai · Contact: hello@amarettolabs.com

What it's good at

Literary prose — scene writing, character voice, emotional interiority
Writing assistance — editing, tone adjustment, conciseness, clarity, line editing
Continuation — picking up a passage and extending it in the same register
Style transfer — shifting a piece between registers (formal ↔ casual, journalistic ↔ personal)

Intended use

Amaretto 8B is a general-purpose writing companion. It works out of the box with no system prompt. For the strongest literary output, you can use the system prompt it was trained with:

You are a skilled literary fiction writer. Write with precision,
emotional depth, and a strong sense of voice. Favor showing over telling.
Use plain, direct language — avoid similes, forced comparisons, and
decorative imagery.

You can replace or extend this prompt for your own use case.

Usage

With llama.cpp / Ollama (GGUF)

GGUF quantized versions are available in this repository:

File	Size	Notes
`amaretto-8b-Q8_0.gguf`	~8.6 GB	Near-lossless; recommended where memory allows
`amaretto-8b-Q5_K_M.gguf`	~5.7 GB	Strong quality on everyday hardware; fits Jetson Orin Nano 8GB
`amaretto-8b-Q4_K_M.gguf`	~4.9 GB	Smallest recommended quantization

llama-cli -m amaretto-8b-Q8_0.gguf \
  --ctx-size 8192 \
  -sys "You are a skilled literary fiction writer." \
  -p "Write the opening of a scene where a woman returns to her childhood home."

Amaretto 8B inherits its base model's full context window via YaRN RoPE scaling, though fine-tuning was done at a 2,048-token sequence length — long-context generation quality on creative-writing tasks has not been specifically validated. --ctx-size above can be raised as needed.

Sampling: --temp 0.8 --min-p 0.1 works well for creative writing. min-p sampling is recommended over top-k/top-p — it noticeably reduces token-level glitches on small models.

With `transformers`

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "AmarettoLabs/Amaretto-8B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [
    {"role": "system", "content": "You are a skilled literary fiction writer. Write with precision, emotional depth, and a strong sense of voice."},
    {"role": "user", "content": "Write the opening of a scene where a woman returns to her childhood home after many years away."},
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
output = model.generate(input_ids, max_new_tokens=512, temperature=0.8, do_sample=True)
print(tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True))

Training details

Property	Value
Base model	mistralai/Ministral-3-8B-Instruct-2512
Method	QLoRA (4-bit NF4)
Framework	Unsloth + TRL SFTTrainer
LoRA rank / alpha	16 / 16
Epochs	3
Training sequence length	2048
Native context length	262,144 (YaRN-scaled; inherited from base model)
Training examples	~14,300
Eval loss	1.354

Training data consists of semi-synthetic and full-synthetic literary prose examples, writing assistance examples (editing, tone, clarity, continuation).

Limitations

Optimized for English prose; other languages are not a focus
Outputs can reflect biases present in the base model and training data
The model may occasionally ignore system prompt instructions on complex or conflicting requests

About AmarettoLabs

AmarettoLabs is a small independent group based in California, USA, specializing in small language models and AI on the edge. The Amaretto line is released free and open.

Website: amaretto.ai · Contact: hello@amarettolabs.com

License

This model is released under the Apache 2.0 License, inherited from the base model. Base model: Ministral 3 8B by Mistral AI.

Downloads last month: 290

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for AmarettoLabs/Amaretto-8B

Base model

mistralai/Ministral-3-8B-Base-2512

Quantized

mistralai/Ministral-3-8B-Instruct-2512

Quantized

(34)

this model

Quantizations

2 models

Amaretto 8B

What it's good at

Intended use

Usage

With llama.cpp / Ollama (GGUF)

With transformers

Training details

Limitations

About AmarettoLabs

License

Model tree for AmarettoLabs/Amaretto-8B

With `transformers`