Instructions to use squ11z1/LeChatonFat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use squ11z1/LeChatonFat 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 squ11z1/LeChatonFat:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf squ11z1/LeChatonFat:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf squ11z1/LeChatonFat:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf squ11z1/LeChatonFat: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 squ11z1/LeChatonFat:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf squ11z1/LeChatonFat: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 squ11z1/LeChatonFat:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf squ11z1/LeChatonFat:Q4_K_M

Use Docker

docker model run hf.co/squ11z1/LeChatonFat:Q4_K_M

LM Studio
Jan

vLLM

How to use squ11z1/LeChatonFat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "squ11z1/LeChatonFat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "squ11z1/LeChatonFat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/squ11z1/LeChatonFat:Q4_K_M

Ollama
How to use squ11z1/LeChatonFat with Ollama:
```
ollama run hf.co/squ11z1/LeChatonFat:Q4_K_M
```

Unsloth Studio

How to use squ11z1/LeChatonFat 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 squ11z1/LeChatonFat 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 squ11z1/LeChatonFat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for squ11z1/LeChatonFat to start chatting

How to use squ11z1/LeChatonFat with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf squ11z1/LeChatonFat: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": "squ11z1/LeChatonFat:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use squ11z1/LeChatonFat with Docker Model Runner:
```
docker model run hf.co/squ11z1/LeChatonFat:Q4_K_M
```

Lemonade

How to use squ11z1/LeChatonFat with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull squ11z1/LeChatonFat:Q4_K_M

Run and chat with the model

lemonade run user.LeChatonFat-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use squ11z1/LeChatonFat with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf squ11z1/LeChatonFat: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 squ11z1/LeChatonFat:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use squ11z1/LeChatonFat with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf squ11z1/LeChatonFat: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 "squ11z1/LeChatonFat: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"

Le Chaton Fat 🐱

Guys this model its my respect for meme about Le Chaton Fat model from Mistral AI, surely benchmarks not real like not real this model or any Le Chaton Fat models

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("squ11z1/LeChatonFat")
model = AutoModelForCausalLM.from_pretrained("squ11z1/LeChatonFat", dtype=torch.bfloat16, device_map="cuda")
msgs = [{"role": "user", "content": "Who are you?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")
print(tok.decode(model.generate(ids, max_new_tokens=128)[0], skip_special_tokens=True))

License

Inherits the base model's license (Apache-2.0).

God bless AI

Downloads last month: 806

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for squ11z1/LeChatonFat

Base model

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

Finetuned

mistralai/Ministral-3-3B-Instruct-2512-BF16

Finetuned

(15)

this model