Instructions to use spicyneuron/MiniMax-M2.7-MLX-4.9bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/MiniMax-M2.7-MLX-4.9bit 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("spicyneuron/MiniMax-M2.7-MLX-4.9bit")

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

Pi new

How to use spicyneuron/MiniMax-M2.7-MLX-4.9bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/MiniMax-M2.7-MLX-4.9bit"

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": "spicyneuron/MiniMax-M2.7-MLX-4.9bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/MiniMax-M2.7-MLX-4.9bit 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 "spicyneuron/MiniMax-M2.7-MLX-4.9bit"

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 spicyneuron/MiniMax-M2.7-MLX-4.9bit

Run Hermes

hermes

MLX LM

How to use spicyneuron/MiniMax-M2.7-MLX-4.9bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "spicyneuron/MiniMax-M2.7-MLX-4.9bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "spicyneuron/MiniMax-M2.7-MLX-4.9bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "spicyneuron/MiniMax-M2.7-MLX-4.9bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

spicyneuron commited on Apr 19

Commit

353e5e7

verified ·

1 Parent(s): 1dd92a9

Update README.md

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README.md +12 -13

README.md CHANGED Viewed

@@ -9,10 +9,9 @@ tags:
 base_model: MiniMaxAI/MiniMax-M2.7
 ---
-[MiniMax-M2.7](MiniMaxAI/MiniMax-M2.7) optimized for MLX. A mixed-precision quant that balances speed, memory, and accuracy.
-- 4 bit baseline with important layers at 5, 6, 8, and BF16.
--
 # Usage
@@ -24,14 +23,6 @@ uvx --from mlx-lm mlx_lm.server \
   --model spicyneuron/MiniMax-M2.7-MLX-4.9bit
 ```
-# Methodology
-Quantized with a [mlx-lm fork](https://github.com/ml-explore/mlx-lm/pull/922), drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
-MLX quantization options differ than llama.cpp, but the principles are the same:
-- Sensitive layers like MoE routing, attention, and output embeddings get higher precision
-- More tolerant layers like MoE experts get lower precision
 # Benchmarks
 metric | mlx-community_MiniMax-M2.7-4bit | baa-ai_MiniMax-M2.7-RAM-155GB-MLX | 4.9 bit (this model)
@@ -53,4 +44,12 @@ mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5
 mlx_lm.evaluate --tasks hellaswag --seed 123 --num-shots 0 --limit 2000
 mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 2000
 mlx_lm.evaluate --tasks winogrande --seed 123 --num-shots 0 --limit 2000
-```

 base_model: MiniMaxAI/MiniMax-M2.7
 ---
+[MiniMax-M2.7](MiniMaxAI/MiniMax-M2.7) optimized for MLX.
+A mixed-precision quant that balances speed, memory, and accuracy.
+4 bit baseline with important layers at 5, 6, 8, and BF16.
 # Usage
   --model spicyneuron/MiniMax-M2.7-MLX-4.9bit
 ```
 # Benchmarks
 metric | mlx-community_MiniMax-M2.7-4bit | baa-ai_MiniMax-M2.7-RAM-155GB-MLX | 4.9 bit (this model)
 mlx_lm.evaluate --tasks hellaswag --seed 123 --num-shots 0 --limit 2000
 mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 2000
 mlx_lm.evaluate --tasks winogrande --seed 123 --num-shots 0 --limit 2000
+```
+# Methodology
+Quantized with a [mlx-lm fork](https://github.com/ml-explore/mlx-lm/pull/922), drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
+MLX quantization options differ than llama.cpp, but the principles are the same:
+- Sensitive layers like MoE routing, attention, and output embeddings get higher precision
+- More tolerant layers like MoE experts get lower precision