Instructions to use osirisbrain/OsirisPtah-Coder-v7-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osirisbrain/OsirisPtah-Coder-v7-MLX 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("osirisbrain/OsirisPtah-Coder-v7-MLX")

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
LM Studio

How to use osirisbrain/OsirisPtah-Coder-v7-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "osirisbrain/OsirisPtah-Coder-v7-MLX"

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": "osirisbrain/OsirisPtah-Coder-v7-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use osirisbrain/OsirisPtah-Coder-v7-MLX 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 "osirisbrain/OsirisPtah-Coder-v7-MLX"

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 osirisbrain/OsirisPtah-Coder-v7-MLX

Run Hermes

hermes

MLX LM

How to use osirisbrain/OsirisPtah-Coder-v7-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "osirisbrain/OsirisPtah-Coder-v7-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "osirisbrain/OsirisPtah-Coder-v7-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "osirisbrain/OsirisPtah-Coder-v7-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

OsirisPtah-Coder-v7-MLX

The Ptah — Osiris's dedicated coding and hacking brain. Fully uncensored (abliterated). Runs natively on Apple Silicon via MLX Metal.

Architecture

Base Model: Qwen2.5-Coder-7B-Instruct (7 billion parameters)
Modification: Abliterated by huihui-ai, converted to MLX 4-bit by OsirisBrain
Format: MLX 4-bit quantized (4.501 bits/weight)
Size: ~4.0 GB
Speed: ~120-180 tokens/sec on M2 Pro (MLX Metal)
Specialization: Code generation, debugging, security analysis, full-stack development

Usage

from mlx_lm import load, generate

model, tokenizer = load("osirisbrain/OsirisPtah-Coder-v7-MLX")
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Write a TypeScript WebSocket server"}],
    add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, max_tokens=2048)

Credits

Abliterated by huihui-ai. Original model: Qwen/Qwen2.5-Coder-7B-Instruct by Alibaba.

Downloads last month: 11

Safetensors

Model size

1B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for osirisbrain/OsirisPtah-Coder-v7-MLX

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

(192)

this model