Instructions to use kernelpool/LongCat-2.0-3bit-UVMAX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kernelpool/LongCat-2.0-3bit-UVMAX 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("kernelpool/LongCat-2.0-3bit-UVMAX")

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 kernelpool/LongCat-2.0-3bit-UVMAX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "kernelpool/LongCat-2.0-3bit-UVMAX"

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": "kernelpool/LongCat-2.0-3bit-UVMAX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kernelpool/LongCat-2.0-3bit-UVMAX 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 "kernelpool/LongCat-2.0-3bit-UVMAX"

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 kernelpool/LongCat-2.0-3bit-UVMAX

Run Hermes

hermes

OpenClaw new

How to use kernelpool/LongCat-2.0-3bit-UVMAX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "kernelpool/LongCat-2.0-3bit-UVMAX"

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 "kernelpool/LongCat-2.0-3bit-UVMAX" \
  --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 kernelpool/LongCat-2.0-3bit-UVMAX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "kernelpool/LongCat-2.0-3bit-UVMAX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "kernelpool/LongCat-2.0-3bit-UVMAX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "kernelpool/LongCat-2.0-3bit-UVMAX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

LongCat-2.0-3bit-UVMAX / README.md

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

	# kernelpool/LongCat-2.0-3bit-UVMAX

	Mixed-precision (UVMAX) quantization of [meituan-longcat/LongCat-2.0](https://huggingface.co/meituan-longcat/LongCat-2.0),
	converted with [mlx-lm](https://github.com/ml-explore/mlx-lm).

	> Revision note: originally converted from the FP8 release
	> ([meituan-longcat/LongCat-2.0-FP8](https://huggingface.co/meituan-longcat/LongCat-2.0-FP8)),
	> the current revision is re-converted from the bf16 master checkpoint.

	## What is UVMAX?

	UVMAX is a mixed-precision scheme: bit widths are assigned per tensor class
	from measured round-trip quantization error, rather than uniformly. All
	classes use group size 64.

	\| Tensor class \| Bits \| Parameters \| Size \| Share \|
	\|---\|---\|---\|---\|---\|
	\| Expert FFNs \| 3 \| 1.47 T \| 598.5 GiB \| 88.0% \|
	\| N-gram embedding tables \| 3 \| 135 B \| 55.0 GiB \| 8.1% \|
	\| Attention, dense MLPs \| 6 \| 31.4 B \| 22.9 GiB \| 3.4% \|
	\| Embeddings, `lm_head` \| 6 \| 2.8 B \| 2.1 GiB \| 0.3% \|
	\| DSA indexer, MoE routers \| 8 \| 0.6 B \| 0.6 GiB \| 0.1% \|
	\| Norms, correction biases (unquantized) \| — \| — \| 0.9 GiB \| 0.1% \|

	## Use with mlx

	This model requires LongCat-2.0 support from [mlx-lm PR #1464](https://github.com/ml-explore/mlx-lm/pull/1464),
	which has not yet been merged. Until it is included in an mlx-lm release, install
	mlx-lm from the PR branch:

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

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("kernelpool/LongCat-2.0-3bit-UVMAX")

	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)
	```