---
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](https://huggingface.co/TOTORONG/LongCat-Flash-3.5bits) was
converted to MLX format from [meituan-longcat/LongCat-Flash-Chat](https://huggingface.co/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.<idx>.*): 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

```bash
pip install mlx-lm
```

```python
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)
```