Instructions to use h1st0ry3D/Godoter-27B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h1st0ry3D/Godoter-27B-MLX-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("h1st0ry3D/Godoter-27B-MLX-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

Unsloth Studio

How to use h1st0ry3D/Godoter-27B-MLX-4bit 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 h1st0ry3D/Godoter-27B-MLX-4bit 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 h1st0ry3D/Godoter-27B-MLX-4bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for h1st0ry3D/Godoter-27B-MLX-4bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="h1st0ry3D/Godoter-27B-MLX-4bit",
    max_seq_length=2048,
)

How to use h1st0ry3D/Godoter-27B-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "h1st0ry3D/Godoter-27B-MLX-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "h1st0ry3D/Godoter-27B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use h1st0ry3D/Godoter-27B-MLX-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "h1st0ry3D/Godoter-27B-MLX-4bit"

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 h1st0ry3D/Godoter-27B-MLX-4bit

Run Hermes

hermes

OpenClaw new

How to use h1st0ry3D/Godoter-27B-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "h1st0ry3D/Godoter-27B-MLX-4bit"

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 "h1st0ry3D/Godoter-27B-MLX-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

MLX LM

How to use h1st0ry3D/Godoter-27B-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "h1st0ry3D/Godoter-27B-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "h1st0ry3D/Godoter-27B-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "h1st0ry3D/Godoter-27B-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Godoter-27B-MLX-4bit

MLX 4-bit quantized version of Ruler97/Godoter-27B — a Qwen3.5-27B finetune specialized for Godot 4 GDScript game development.

Converted with Unsloth 2026.6.6 and quantized to 4-bit (group_size=64, affine mode) for compatibility with oMLX and Apple MLX on Apple Silicon.

Model details

Property	Value
Base model	unsloth/Qwen3.6-27B
Finetune	Ruler97/Godoter-27B — Godot GDScript expert
Architecture	Qwen3.5 (dense 27B, 64 layers, 24 attention heads, 4 KV heads)
Context length	262,144 tokens (theoretical), 32,768 recommended on 32 GB
Quantization	4-bit, group_size=64, affine mode
Weight size	~14 GB (3 safetensors shards)
Format	MLX safetensors (`mlx` library)
MTP	1 MTP prediction head (included in weights)

Files

File	Size	Description
`config.json`	4.2 KB	Architecture + quantization config
`model-00001-of-00003.safetensors`	5.0 GB	Weight shard 1
`model-00002-of-00003.safetensors`	5.0 GB	Weight shard 2
`model-00003-of-00003.safetensors`	4.1 GB	Weight shard 3
`model.safetensors.index.json`	185 KB	Weight map index
`tokenizer.json`	19 MB	Tokenizer
`tokenizer_config.json`	1.2 KB	Tokenizer config
`chat_template.jinja`	7.9 KB	Qwen3.5 chat template
`generation_config.json`	213 B	Generation defaults

Notes

This is a pure text model (the vision tower from Qwen3.5-VL is excluded from the quant — only the text decoder was converted)
The model has MTP (Multi-Token Prediction) heads but can be run without them
For 64k context, use the GGUF version instead (llama.cpp handles KV cache more efficiently for dense models)
Converted with Unsloth 2026.6.6 (unsloth_fixed_mtp: true)