Instructions to use hitonet/hito-1.7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hitonet/hito-1.7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hitonet/hito-1.7b-GGUF",
	filename="hito-1.7b-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use hitonet/hito-1.7b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hitonet/hito-1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hitonet/hito-1.7b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hitonet/hito-1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hitonet/hito-1.7b-GGUF: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 hitonet/hito-1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hitonet/hito-1.7b-GGUF: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 hitonet/hito-1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hitonet/hito-1.7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/hitonet/hito-1.7b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use hitonet/hito-1.7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hitonet/hito-1.7b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hitonet/hito-1.7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hitonet/hito-1.7b-GGUF:Q4_K_M

Ollama
How to use hitonet/hito-1.7b-GGUF with Ollama:
```
ollama run hf.co/hitonet/hito-1.7b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use hitonet/hito-1.7b-GGUF 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 hitonet/hito-1.7b-GGUF 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 hitonet/hito-1.7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hitonet/hito-1.7b-GGUF to start chatting

Pi new

How to use hitonet/hito-1.7b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf hitonet/hito-1.7b-GGUF: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": "hitonet/hito-1.7b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hitonet/hito-1.7b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf hitonet/hito-1.7b-GGUF: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 hitonet/hito-1.7b-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use hitonet/hito-1.7b-GGUF with Docker Model Runner:
```
docker model run hf.co/hitonet/hito-1.7b-GGUF:Q4_K_M
```

Lemonade

How to use hitonet/hito-1.7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hitonet/hito-1.7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.hito-1.7b-GGUF-Q4_K_M

List all available models

lemonade list

Hito 1.7B - GGUF

Quantized versions for llama.cpp, Ollama, LM Studio, and more

About

This repository contains GGUF quantized versions of hitonet/hito-1.7b.

Hito is a 1.7B parameter model with structured thinking patterns that enable better accuracy and transparency.

For the original model (safetensors), training details, benchmarks, and full documentation, see the main repository.

Available Quantizations

File	Quant	Size	Quality	Notes
hito-1.7b-Q4_K_M.gguf	Q4_K_M	1.1 GB	BEST	Perfect balance of size and quality
hito-1.7b-Q5_K_M.gguf	Q5_K_M	1.2 GB	Excellent	Slightly better than Q4_K_M
hito-1.7b-Q8_0.gguf	Q8_0	1.8 GB	Excellent	Highest quality quantization

Good Quality

File	Quant	Size	Quality	Notes
hito-1.7b-Q4_0.gguf	Q4_0	1.0 GB	Good	Legacy format, works well
hito-1.7b-Q4_K_S.gguf	Q4_K_S	1.0 GB	Good	Smaller Q4 variant
hito-1.7b-Q5_0.gguf	Q5_0	1.2 GB	Good	Legacy 5-bit
hito-1.7b-Q5_K_S.gguf	Q5_K_S	1.2 GB	Good	Smaller Q5 variant
hito-1.7b-Q6_K.gguf	Q6_K	1.4 GB	Excellent	Near full quality
hito-1.7b-F16.gguf	F16	3.3 GB	Reference	Full precision GGUF

Low Quality (Not Recommended)

File	Quant	Size	Quality	Notes
hito-1.7b-Q3_K_L.gguf	Q3_K_L	957 MB	Fair	May get stuck in thinking
hito-1.7b-Q3_K_M.gguf	Q3_K_M	896 MB	Fair	Occasional issues
hito-1.7b-Q3_K_S.gguf	Q3_K_S	827 MB	Fair	Noticeable quality loss

Broken (Do Not Use)

File	Quant	Size	Quality	Notes
hito-1.7b-Q2_K.gguf	Q2_K	742 MB	Broken	Produces gibberish

Quick Start

Ollama

# Download the recommended quantization
wget https://huggingface.co/hitonet/hito-1.7b-GGUF/resolve/main/hito-1.7b-Q4_K_M.gguf

# Create Modelfile
cat > Modelfile << 'EOF'
FROM hito-1.7b-Q4_K_M.gguf
SYSTEM "You are Hito by Hitonet.com."
PARAMETER temperature 0.7
PARAMETER stop "<|im_end|>"
EOF

# Create and run
ollama create hito -f Modelfile
ollama run hito

llama.cpp

./llama-cli -m hito-1.7b-Q4_K_M.gguf \
  -sys "You are Hito by Hitonet.com." \
  -p "What is your name?" \
  -n 256

LM Studio

Download any GGUF file from this repository
Open LM Studio → Load Model
Set system prompt: You are Hito by Hitonet.com.
Start chatting!

Compatibility

These GGUF files work with:

Ollama (recommended)
llama.cpp
LM Studio
Jan
GPT4All
llama-cpp-python
Any llama.cpp-compatible application

What Makes Hito Special

Structured Thinking: Uses <think> tags for transparent reasoning
Self-Correcting: Catches errors mid-reasoning
Humble by Design: Admits uncertainty rather than hallucinating
Efficient: Only 1.7B parameters, runs on CPU

For full documentation, benchmarks, and training details, see the main repository.

Licensing

Component	License	Commercial Use
Model Weights	Apache 2.0	✅ Free to use
Training Methodology	Proprietary	⚠️ Commercial License Required

Model Weights (Apache 2.0)

The model weights are open source under Apache 2.0. You may use, modify, and distribute them freely.

Training Methodology (Commercial License Required)

The training methodology and cognitive framework used to create this model are proprietary to Hitonet.

Commercial use of the training methodology requires a license.

Attribution is mandatory when using this model or discussing its capabilities.

For commercial licensing inquiries: legal@hitonet.com

Made with genuine curiosity by Hitonet

Downloads last month: 289

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for hitonet/hito-1.7b-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

hitonet/hito-1.7b

Quantized

(4)

this model