How to use from the
Use from the
MLX library
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("abnormalmapstudio/Qwen2.5-Coder-1.5B-Instruct-mxfp4-mlx")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

abnormalmapstudio/Qwen2.5-Coder-1.5B-Instruct-mxfp4-mlx

This model abnormalmapstudio/Qwen2.5-Coder-1.5B-Instruct-mxfp4-mlx was converted to MLX format from Qwen/Qwen2.5-Coder-1.5B-Instruct using mlx-lm version 0.28.1.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("abnormalmapstudio/Qwen2.5-Coder-1.5B-Instruct-mxfp4-mlx")

prompt = "hello"

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

response = generate(model, tokenizer, prompt=prompt, verbose=True)
Downloads last month
86
Safetensors
Model size
0.3B params
Tensor type
U8
U32
BF16
MLX
Hardware compatibility
Log In to add your hardware

Quantized

Inference Providers NEW
This model isn't deployed by any Inference Provider. 馃檵 Ask for provider support

Model tree for abnormalmapstudio/Qwen2.5-Coder-1.5B-Instruct-mxfp4-mlx

Finetuned
(159)
this model