Instructions to use ssdataanalysis/Hebatron-mlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ssdataanalysis/Hebatron-mlx-fp16 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-fp16")

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-fp16 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-fp16"

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-fp16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ssdataanalysis/Hebatron-mlx-fp16 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-fp16"

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-fp16

Run Hermes

hermes

MLX LM

How to use ssdataanalysis/Hebatron-mlx-fp16 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-fp16"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "ssdataanalysis/Hebatron-mlx-fp16"
# 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-fp16",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hebatron-mlx-fp16 / README.md

ssdataanalysis

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metadata

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-fp16

The Model ssdataanalysis/Hebatron-mlx-fp16 was converted to MLX format from HebArabNlpProject/Hebatron using mlx-lm version 0.31.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("ssdataanalysis/Hebatron-mlx-fp16")

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)