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kernelpool/LongCat-2.0-3bit-UVMAX
LongCat-2.0-3bit-UVMAX is a text generation model from kernelpool. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as mit.
Mixed-precision (UVMAX) quantization of meituan-longcat/LongCat-2.0, converted with mlx-lm.
Downloads · 30 days
368
40% of all-time downloads
All-time downloads
919
Public
Parameters
1.6T
1.4 TB on disk
Likes
1
Public
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.safetensors730 GB · 100%
How the weights are stored.
U321.6T · 100%
From the Hugging Face model README
Mixed-precision (UVMAX) quantization of meituan-longcat/LongCat-2.0, converted with mlx-lm.
Revision note: originally converted from the FP8 release (meituan-longcat/LongCat-2.0-FP8), the current revision is re-converted from the bf16 master checkpoint.
UVMAX is a mixed-precision scheme: bit widths are assigned per tensor class from measured round-trip quantization error, rather than uniformly. All classes use group size 64.
| Tensor class | Bits | Parameters | Size | Share |
|---|---|---|---|---|
| Expert FFNs | 3 | 1.47 T | 598.5 GiB | 88.0% |
| N-gram embedding tables | 3 | 135 B | 55.0 GiB | 8.1% |
| Attention, dense MLPs | 6 | 31.4 B | 22.9 GiB | 3.4% |
Embeddings, lm_head | 6 | 2.8 B | 2.1 GiB | 0.3% |
| DSA indexer, MoE routers | 8 | 0.6 B | 0.6 GiB | 0.1% |
| Norms, correction biases (unquantized) | — | — | 0.9 GiB | 0.1% |
This model requires LongCat-2.0 support from mlx-lm PR #1464, which has not yet been merged. Until it is included in an mlx-lm release, install mlx-lm from the PR branch:
pip install git+https://github.com/ml-explore/mlx-lm.git@refs/pull/1464/head
from mlx_lm import load, generate
model, tokenizer = load("kernelpool/LongCat-2.0-3bit-UVMAX")
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)