Instructions to use ailexleon/Assistant_Pepe_70B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ailexleon/Assistant_Pepe_70B-mlx-4Bit 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("ailexleon/Assistant_Pepe_70B-mlx-4Bit") 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
- LM Studio
- MLX LM
How to use ailexleon/Assistant_Pepe_70B-mlx-4Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ailexleon/Assistant_Pepe_70B-mlx-4Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ailexleon/Assistant_Pepe_70B-mlx-4Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ailexleon/Assistant_Pepe_70B-mlx-4Bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
metadata
language: en
library_name: mlx
base_model: SicariusSicariiStuff/Assistant_Pepe_70B
tags:
- mlx
pipeline_tag: text-generation
widget:
- text: Assistant_Pepe_70B
output:
url: >-
https://huggingface.co/SicariusSicariiStuff/Assistant_Pepe_70B/resolve/main/Images/Assistant_Pepe_70B.png
ailexleon/Assistant_Pepe_70B-mlx-4Bit
Converted to MLX format from SicariusSicariiStuff/Assistant_Pepe_70B using mlx-lm version 0.31.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("ailexleon/Assistant_Pepe_70B-mlx-4Bit")
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)