Instructions to use aidiffuser/Kimi-K2.6-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aidiffuser/Kimi-K2.6-MLX-4bit 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("aidiffuser/Kimi-K2.6-MLX-4bit")

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 aidiffuser/Kimi-K2.6-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "aidiffuser/Kimi-K2.6-MLX-4bit"

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": "aidiffuser/Kimi-K2.6-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use aidiffuser/Kimi-K2.6-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "aidiffuser/Kimi-K2.6-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "aidiffuser/Kimi-K2.6-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "aidiffuser/Kimi-K2.6-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use aidiffuser/Kimi-K2.6-MLX-4bit 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 "aidiffuser/Kimi-K2.6-MLX-4bit"

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 aidiffuser/Kimi-K2.6-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use aidiffuser/Kimi-K2.6-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "aidiffuser/Kimi-K2.6-MLX-4bit"

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 "aidiffuser/Kimi-K2.6-MLX-4bit" \
  --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"

Kimi-K2.6-mlx

MLX-compatible weights for moonshotai/Kimi-K2.6, converted using mlx_lm.convert.

Model Details

Base model: moonshotai/Kimi-K2.6
Architecture: Mixture-of-Experts (MoE), Kimi/DeepSeek family
Layers: 61
Hidden size: 7168
KV heads: 64
Quantization: INT4 compressed-tensors (pack-quantized)
Model size on disk: ~612 GB (182 safetensors shards)
Context length: 262,144 tokens
Capabilities: text generation, thinking/reasoning, tool use (text-only; vision not supported — use mlx-vlm for VLM conversion)

Conversion

Converted from the original moonshotai/Kimi-K2.6 using:

python -m mlx_lm.convert \
  --hf-path moonshotai/Kimi-K2.6 \
  --mlx-path ./Kimi-K2.6-mlx \
  --trust-remote-code

No additional quantization was applied — the model ships with compressed-tensors INT4 from the source.

Usage

With mlx-lm

pip install mlx-lm
python -m mlx_lm.generate \
  --model aidiffuser/Kimi-K2.6-mlx \
  --prompt "Hello, who are you?" \
  --trust-remote-code

With exo (distributed inference)

This model runs on exo with JACCL/RDMA tensor parallelism across multiple Apple Silicon nodes. Tested on a 2-node Mac Studio M3 Ultra cluster (512 GB + 512 GB unified memory) at ~21 tok/s.

Recommended sampling parameters

temperature: 1.0
top_p: 0.95
min_p: 0.01

Hardware Requirements

This is a large MoE model. You will need significant unified memory to run it:

Distributed: Two Apple Silicon machines with 512+ GB each, connected via Thunderbolt

License

This model inherits the Kimi K2 Community License from the base model.

Downloads last month: 205

Safetensors

Model size

1T params

Tensor type

BF16

F32

U32

MLX

Hardware compatibility

4-bit

Model tree for aidiffuser/Kimi-K2.6-MLX-4bit

Base model

moonshotai/Kimi-K2.6

Quantized

(45)

this model