metadata
license: mit
library_name: mlx
pipeline_tag: text-generation
tags:
- transformers
- mlx
base_model: meituan-longcat/LongCat-Flash-Chat
TOTORONG/LongCat-Flash-3.5bits
This model TOTORONG/LongCat-Flash-3.5bits was converted to MLX format from meituan-longcat/LongCat-Flash-Chat using mlx-lm version 0.27.1.
#Quantized model with 3.516 bits per weight to fit M3 Ultra 256GB
#“Selected layers” (the precision bump mask) #A layer is considered early/late/periodic if its index i (from model.layers.i) satisfies: #i < num_layers // 8 or #i >= 7 * num_layers // 8 or #(i - num_layers // 8) % 3 == 2
#These layers receive: #Q/K/V: 3b → 4b #O-proj: 4b → 6b #Experts (.mlps..*): 2b → 3b #Switch-MLP remains 3b across all layers. #This mask preserves prompt-sensitivity (front) and output stability (tail), with a periodic boost to reduce worst-case error accumulation.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("TOTORONG/LongCat-Flash-3.5bits")
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)