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

f540a40

verified ·

1 Parent(s): 6b20176

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@@ -1,7 +1,7 @@
 ---
 license: other
 base_model: MiniMaxAI/MiniMax-M3
-tags: [mlx, vmlx, jang, reap, awq, moe, code, multimodal, minimax-m3, osaurus, apple-silicon]
 pipeline_tag: text-generation
 ---
@@ -9,11 +9,11 @@ pipeline_tag: text-generation
 <h1 align="center">MiniMax-M3-Coder-Small</h1>
 <p align="center"><b>🦖 Osaurus Exclusive — a compact JANG-quantized MiniMax-M3 coder (coding · agentic · multimodal) for Apple Silicon.</b></p>
-> ⚠️ **Requires vMLX engine v1.5.67+.**
-> This is a **JANG-format** model (JANG affine + **AWQ** quant, **REAP** expert pruning, MiniMax-M3 MSA/Lightning-Indexer runtime). It will **NOT** load with `transformers`, `vLLM`, or generic MLX loaders — it runs on the vMLX engine (ships in **Osaurus**).
 ## What is a JANG model?
-**JANG** is vMLX's quantization + packing format: mixed-precision affine quant with per-projection bit widths + **AWQ** activation-aware scaling + **REAP** expert pruning, via a `jang_config.json`. Weights stay quantized in GPU memory and load through vMLX's JANG loader. The format + the M3 runtime are vMLX-specific, so it **runs only on vMLX 1.5.67 or newer.**
 ## Highlights
 - **Smallest M3 coder — ~84 GB** (the compact Osaurus build).
@@ -23,10 +23,9 @@ pipeline_tag: text-generation
 - Calibration: Vera (agentic-coder) + GSM8K; "floor" recipe keeps the most-salient coding experts.
 ## Run it
-- In **Osaurus** / vMLX 1.5.67+: pick this model, Start, then chat.
-- CLI: `vmlx-engine serve OsaurusAI/MiniMax-M3-Coder-Small --reasoning-parser minimax_m3 --tool-call-parser minimax_m3`
 ## Attribution
 - Base model: **MiniMaxAI/MiniMax-M3** · Pruning: **REAP** (Cerebras, arXiv:2510.13999)
 - **Vera calibration + testing: [@hornsman1](https://huggingface.co/hornsman1) (hornsan1 on GitHub)** · math calibration: GSM8K
-- Quantization & runtime: **JANG / vMLX** · Distributed via **Osaurus**

 ---
 license: other
 base_model: MiniMaxAI/MiniMax-M3
+tags: [mlx, jang, reap, awq, moe, code, multimodal, minimax-m3, osaurus, apple-silicon]
 pipeline_tag: text-generation
 ---
 <h1 align="center">MiniMax-M3-Coder-Small</h1>
 <p align="center"><b>🦖 Osaurus Exclusive — a compact JANG-quantized MiniMax-M3 coder (coding · agentic · multimodal) for Apple Silicon.</b></p>
+> ⚠️ **JANG-format model — runs on Osaurus.**
+> This uses the **JANG** quantization format (mixed-precision affine + **AWQ** + **REAP** expert pruning) and loads through **Osaurus's native Swift runtime**. It will **NOT** load with `transformers`, `vLLM`, or generic MLX loaders.
 ## What is a JANG model?
+**JANG** is a mixed-precision quantization + packing format — per-projection affine bit widths + **AWQ** activation-aware scaling + **REAP** expert pruning — described by a `jang_config.json`. Weights stay quantized in GPU memory. **Osaurus loads it through its native Swift JANG runtime** on Apple Silicon.
 ## Highlights
 - **Smallest M3 coder — ~84 GB** (the compact Osaurus build).
 - 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.
 ## Attribution
 - Base model: **MiniMaxAI/MiniMax-M3** · Pruning: **REAP** (Cerebras, arXiv:2510.13999)
 - **Vera calibration + testing: [@hornsman1](https://huggingface.co/hornsman1) (hornsan1 on GitHub)** · math calibration: GSM8K
+- Quantization: **JANG** · Runtime & distribution: **Osaurus**