Instructions to use pipenetwork/LongCat-2.0-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pipenetwork/LongCat-2.0-2bit 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("pipenetwork/LongCat-2.0-2bit")

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 pipenetwork/LongCat-2.0-2bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pipenetwork/LongCat-2.0-2bit"

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": "pipenetwork/LongCat-2.0-2bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pipenetwork/LongCat-2.0-2bit 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 "pipenetwork/LongCat-2.0-2bit"

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 pipenetwork/LongCat-2.0-2bit

Run Hermes

hermes

OpenClaw new

How to use pipenetwork/LongCat-2.0-2bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pipenetwork/LongCat-2.0-2bit"

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 "pipenetwork/LongCat-2.0-2bit" \
  --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"

MLX LM

How to use pipenetwork/LongCat-2.0-2bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "pipenetwork/LongCat-2.0-2bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "pipenetwork/LongCat-2.0-2bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "pipenetwork/LongCat-2.0-2bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

LongCat-2.0-2bit / README.md

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metadata

language: en
library_name: mlx
pipeline_tag: text-generation
tags:
  - mlx
  - longcat
license: mit
base_model: meituan-longcat/LongCat-2.0-FP8

pipenetwork/LongCat-2.0-2bit

2-bit (2.501 bits/weight) MLX quantization of meituan-longcat/LongCat-2.0, a 1.6T-parameter / ~48B-active MoE (MLA attention + LongCat sparse-attention indexer + identity experts + n-gram embeddings). Converted from the FP8 source with mlx-lm. Router classifiers are kept at 8-bit (mixed precision); MTP layers are dropped.

Size: ~477 GB. This exceeds a 512 GB unified-memory ceiling in practice — intended for larger-memory or sharded/multi-node MLX inference, not a single 512 GB machine.

Requires mlx-lm PR #1464

LongCat-2.0 (model_type: longcat2) support is not yet in a released mlx-lm. Install from the PR branch:

pip install git+https://github.com/ml-explore/mlx-lm.git@refs/pull/1464/head

Use

from mlx_lm import load, generate

model, tokenizer = load("pipenetwork/LongCat-2.0-2bit")
messages = [{"role": "user", "content": "Who is Albert Einstein?"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=512, verbose=True))

For large builds, use sharded/distributed generation (mlx.launch + sharded_generate.py).