Instructions to use mlx-community/MiniMax-M3.1-Flash-Text-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/MiniMax-M3.1-Flash-Text-3bit 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("mlx-community/MiniMax-M3.1-Flash-Text-3bit")

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 mlx-community/MiniMax-M3.1-Flash-Text-3bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/MiniMax-M3.1-Flash-Text-3bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mlx-community/MiniMax-M3.1-Flash-Text-3bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use mlx-community/MiniMax-M3.1-Flash-Text-3bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/MiniMax-M3.1-Flash-Text-3bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/MiniMax-M3.1-Flash-Text-3bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/MiniMax-M3.1-Flash-Text-3bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use mlx-community/MiniMax-M3.1-Flash-Text-3bit 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 "mlx-community/MiniMax-M3.1-Flash-Text-3bit"

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 mlx-community/MiniMax-M3.1-Flash-Text-3bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/MiniMax-M3.1-Flash-Text-3bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/MiniMax-M3.1-Flash-Text-3bit"

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 "mlx-community/MiniMax-M3.1-Flash-Text-3bit" \
  --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"

MiniMax-M3.1-Flash-Text-3bit / README.md

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---
license: other
license_name: minimax-community
license_link: LICENSE
library_name: mlx
pipeline_tag: text-generation
base_model: MiniMaxAI/MiniMax-M3.1-Flash
tags:
- moe
- agent
- coding
- mlx
---
# mlx-community/MiniMax-M3.1-Flash-Text-3bit

This model [mlx-community/MiniMax-M3.1-Flash-Text-3bit](https://huggingface.co/mlx-community/MiniMax-M3.1-Flash-Text-3bit) was converted to MLX format from [MiniMaxAI/MiniMax-M3.1-Flash](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) using mlx-lm with [ml-explore/mlx-lm#2003](https://github.com/ml-explore/mlx-lm/pull/2003). Refer to the [original model card](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) for more details on the model.

This is a text-only conversion. The vision tower is not included.

The original weights were dequantized and then quantized to 3-bit affine with group size 64 (3.5 bits per weight). The router is kept at 8 bits. Peak memory was about 187 GB in a short generation test.

Tested with mlx-lm 0.32.2.

## Use with mlx

```bash
pip install -U mlx-lm
```

```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/MiniMax-M3.1-Flash-Text-3bit")
prompt = "hello"
if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```

	---
	license: other
	license_name: minimax-community
	license_link: LICENSE
	library_name: mlx
	pipeline_tag: text-generation
	base_model: MiniMaxAI/MiniMax-M3.1-Flash
	tags:
	- moe
	- agent
	- coding
	- mlx
	---

	# mlx-community/MiniMax-M3.1-Flash-Text-3bit

	This model [mlx-community/MiniMax-M3.1-Flash-Text-3bit](https://huggingface.co/mlx-community/MiniMax-M3.1-Flash-Text-3bit) was converted to MLX format from [MiniMaxAI/MiniMax-M3.1-Flash](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) using mlx-lm with [ml-explore/mlx-lm#2003](https://github.com/ml-explore/mlx-lm/pull/2003). Refer to the [original model card](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash) for more details on the model.

	This is a text-only conversion. The vision tower is not included.

	The original weights were dequantized and then quantized to 3-bit affine with group size 64 (3.5 bits per weight). The router is kept at 8 bits. Peak memory was about 187 GB in a short generation test.

	Tested with mlx-lm 0.32.2.

	## Use with mlx

	```bash
	pip install -U mlx-lm
	```

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("mlx-community/MiniMax-M3.1-Flash-Text-3bit")

	prompt = "hello"

	if tokenizer.chat_template is not None:
	messages = [{"role": "user", "content": prompt}]
	prompt = tokenizer.apply_chat_template(
	messages, add_generation_prompt=True, return_dict=False,
	)

	response = generate(model, tokenizer, prompt=prompt, verbose=True)
	```