MLX-Optimized-Models
Collection
5 items โข Updated
How to use AlphaOxO/Poro-34B-chat-4bits-mlx 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("AlphaOxO/Poro-34B-chat-4bits-mlx")
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)How to use AlphaOxO/Poro-34B-chat-4bits-mlx with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "AlphaOxO/Poro-34B-chat-4bits-mlx"
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "AlphaOxO/Poro-34B-chat-4bits-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "AlphaOxO/Poro-34B-chat-4bits-mlx",
"messages": [
{"role": "user", "content": "Hello"}
]
}'Quantized to 4 bits per weight.
Converted from LumiOpen/Poro-34B-chat using mlx-lm.
The bloom model type is support in my mlx-lm fork(AlphaKure/mlx-lm.
Until test finish. I will send a PR to offical repo.
Update at: 2026/09/11
pip install git+https://github.com/AlphaKure/mlx-lm.git@64c70971dc5cba1220740218b0126369cdb6034a
mlx_lm.generate --model AlphaOxO/Poro-34B-chat-4bits-mlx --prompt "hello"
from mlx_lm import load, generate
model, tokenizer = load("AlphaOxO/Poro-34B-chat-4bits-mlx")
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "hello"}], add_generation_prompt=True
)
print(generate(model, tokenizer, prompt=prompt))
4-bit
Base model
LumiOpen/Poro-34B-chat