Instructions to use ssdataanalysis/Hebatron-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ssdataanalysis/Hebatron-mlx-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("ssdataanalysis/Hebatron-mlx-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 ssdataanalysis/Hebatron-mlx-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 "ssdataanalysis/Hebatron-mlx-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": "ssdataanalysis/Hebatron-mlx-8Bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ssdataanalysis/Hebatron-mlx-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 "ssdataanalysis/Hebatron-mlx-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 ssdataanalysis/Hebatron-mlx-8Bit

Run Hermes

hermes

MLX LM

How to use ssdataanalysis/Hebatron-mlx-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 "ssdataanalysis/Hebatron-mlx-8Bit"

Run an OpenAI-compatible server

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

Hebatron-mlx-8Bit / README.md

ssdataanalysis

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	---
	language:
	- he
	- en
	license: apache-2.0
	library_name: mamba
	tags:
	- mamba2
	- moe
	- hebrew
	- finance
	- legal
	- ssm
	- mlx
	- mlx-my-repo
	model_name: HEBATRON
	base_model: HebArabNlpProject/Hebatron
	pipeline_tag: text-generation
	---

	# ssdataanalysis/Hebatron-mlx-8Bit

	The Model [ssdataanalysis/Hebatron-mlx-8Bit](https://huggingface.co/ssdataanalysis/Hebatron-mlx-8Bit) was converted to MLX format from [HebArabNlpProject/Hebatron](https://huggingface.co/HebArabNlpProject/Hebatron) using mlx-lm version 0.31.2.

	## Use with mlx

	```bash
	pip install mlx-lm
	```

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("ssdataanalysis/Hebatron-mlx-8Bit")

	prompt="hello"

	if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
	messages = [{"role": "user", "content": prompt}]
	prompt = tokenizer.apply_chat_template(
	messages, tokenize=False, add_generation_prompt=True
	)

	response = generate(model, tokenizer, prompt=prompt, verbose=True)
	```