Instructions to use OsaurusAI/MiniMax-M3-Coder-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/MiniMax-M3-Coder-Small 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("OsaurusAI/MiniMax-M3-Coder-Small")

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

How to use OsaurusAI/MiniMax-M3-Coder-Small with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/MiniMax-M3-Coder-Small"

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": "OsaurusAI/MiniMax-M3-Coder-Small"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OsaurusAI/MiniMax-M3-Coder-Small 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 "OsaurusAI/MiniMax-M3-Coder-Small"

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 OsaurusAI/MiniMax-M3-Coder-Small

Run Hermes

hermes

MLX LM

How to use OsaurusAI/MiniMax-M3-Coder-Small with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "OsaurusAI/MiniMax-M3-Coder-Small"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "OsaurusAI/MiniMax-M3-Coder-Small"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "OsaurusAI/MiniMax-M3-Coder-Small",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Osaurus-AI commited on 9 days ago

Commit

33cc8fc

verified ·

1 Parent(s): f540a40

Add HumanEval pass@1=100% benchmark

Browse files

Files changed (1) hide show

README.md +4 -0

README.md CHANGED Viewed

@@ -22,6 +22,10 @@ pipeline_tag: text-generation
 - **Multimodal (vision) kept.**
 - Calibration: Vera (agentic-coder) + GSM8K; "floor" recipe keeps the most-salient coding experts.
 ## Run it
 Load it in **Osaurus** (Apple Silicon) — it runs on Osaurus's native Swift JANG runtime.

 - **Multimodal (vision) kept.**
 - Calibration: Vera (agentic-coder) + GSM8K; "floor" recipe keeps the most-salient coding experts.
+## Benchmarks
+- **HumanEval: pass@1 = 100%** (82/82, scrambled-half adaptive eval, seed 42; 0 failures, 0 escalations).
+- Despite 45% expert pruning + all-2-bit routed experts, coding accuracy holds at **100%** — the REAP45 keep-set is a subset of the larger M3-Coder builds' proven coding experts, so coding capability is preserved while the model shrinks to ~84 GB.
 ## Run it
 Load it in **Osaurus** (Apple Silicon) — it runs on Osaurus's native Swift JANG runtime.