Instructions to use kernelpool/Hy3-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kernelpool/Hy3-8bit 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/Hy3-8bit")

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/Hy3-8bit 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/Hy3-8bit"

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/Hy3-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kernelpool/Hy3-8bit 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/Hy3-8bit"

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/Hy3-8bit

Run Hermes

hermes

OpenClaw new

How to use kernelpool/Hy3-8bit 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/Hy3-8bit"

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/Hy3-8bit" \
  --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/Hy3-8bit 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/Hy3-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "kernelpool/Hy3-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "kernelpool/Hy3-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hy3-8bit / README.md

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	---
	language: en
	library_name: mlx
	pipeline_tag: text-generation
	tags:
	- mlx
	- hunyuan
	- hy3
	- moe
	- text-generation
	license: apache-2.0
	base_model: tencent/Hy3
	---

	# kernelpool/Hy3-8bit

	This model [kernelpool/Hy3-8bit](https://huggingface.co/kernelpool/Hy3-8bit) was
	converted to MLX format from [tencent/Hy3](https://huggingface.co/tencent/Hy3)
	using mlx-lm version 0.31.3.

	## Use with mlx

	This model requires Hy3 support from [mlx-lm PR #1211](https://github.com/ml-explore/mlx-lm/pull/1211),
	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/1211/head
	```

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("kernelpool/Hy3-8bit")

	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)
	```