Instructions to use spicyneuron/Kimi-K2.7-Code-MLX-3.6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/Kimi-K2.7-Code-MLX-3.6bit 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/Kimi-K2.7-Code-MLX-3.6bit")

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 spicyneuron/Kimi-K2.7-Code-MLX-3.6bit 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/Kimi-K2.7-Code-MLX-3.6bit"

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/Kimi-K2.7-Code-MLX-3.6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/Kimi-K2.7-Code-MLX-3.6bit 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/Kimi-K2.7-Code-MLX-3.6bit"

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/Kimi-K2.7-Code-MLX-3.6bit

Run Hermes

hermes

MLX LM

How to use spicyneuron/Kimi-K2.7-Code-MLX-3.6bit 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/Kimi-K2.7-Code-MLX-3.6bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "spicyneuron/Kimi-K2.7-Code-MLX-3.6bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "spicyneuron/Kimi-K2.7-Code-MLX-3.6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

moonshotai/Kimi-K2.7-Code optimized for running on a Mac Studio M3 Ultra.

A mixed-precision quant that balances speed, memory, and accuracy.
3-bit MoE baseline with important always-on layers at higher precision.
Fits into ~460 GB memory, leaving enough room for a smaller utility model.

Usage

# Start server at http://localhost:8080/v1/chat/completions
uvx --from mlx-lm mlx_lm.server \
  --host 127.0.0.1 \
  --port 8080 \
  --model spicyneuron/Kimi-K2.7-Code-MLX-3.6bit

Benchmarks

metric	this model
bpw	3.578
base memory	427.579
peak memory (1024/512)	460.444
prompt tok/s (1024)	218.851 ± 0.208
gen tok/s (512)	21.035 ± 0.049
perplexity	4.462 ± 0.037
arc_challenge	0.692 ± 0.021
hellaswag	0.780 ± 0.019

Methodology

Quantized with a mlx-lm fork. 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

Downloads last month: 1,824

Safetensors

Model size

1T params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit

Model tree for spicyneuron/Kimi-K2.7-Code-MLX-3.6bit

Base model

moonshotai/Kimi-K2.7-Code

Quantized

(14)

this model