README.md

metadata

license: mit
language:
  - en
base_model:
  - inclusionAI/Ring-mini-linear-2.0
pipeline_tag: text-generation

Quantized Ring-Linear-2.0

Introduction

To enable deployment of Ring-Linear-2.0 on memory-constrained devices, we release quantized weights using the GPTQ INT4 format. Additionally, we evaluate the online FP8 quantization performance of Ring-Linear-2.0 models, which closely approaches that of BF16 precision.

Model Downloads

Model	Maximum Supported Length	Download
Ring-flash-linear-2.0-GPTQ-int4	128k	🤗 HuggingFace 🤖 ModelScope
Ring-mini-linear-2.0-GPTQ-int4	512k	🤗 HuggingFace 🤖 ModelScope

Quickstart

🚀 vLLM

Environment Preparation

Since the Pull Request (PR) has not been submitted to the vLLM community at this stage, please prepare the environment by following the steps below.

First, create a Conda environment with Python 3.10 and CUDA 12.8 (Use the root or admin account, or ensure the current user has access to /home/admin/logs):

conda create -n vllm python=3.10
conda activate vllm

Next, install our vLLM wheel package:

pip install https://media.githubusercontent.com/media/inclusionAI/Ring-V2/refs/heads/main/hybrid_linear/whls/vllm-0.8.5%2Bcuda12_8_gcc10_2_1-cp310-cp310-linux_x86_64.whl --force-reinstall

Finally, install compatible versions of PyTorch and Torchvision after vLLM is installed:

pip install torch==2.7.0 torchvision==0.22.0 

Offline Inference

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

if __name__ == '__main__':
    tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ring-mini-linear-2.0-GPTQ-int4")
    
    sampling_params = SamplingParams(temperature=0.6, top_p=1.0, max_tokens=16384)

    # use `max_num_seqs=1` without concurrency
    llm = LLM(model="inclusionAI/Ring-mini-linear-2.0-GPTQ-int4", dtype='auto', enable_prefix_caching=False, max_num_seqs=128)
    
    
    prompt = "Give me a short introduction to large language models."
    messages = [
        {"role": "user", "content": prompt}
    ]
    
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    outputs = llm.generate([text], sampling_params)
    for output in outputs:
        print(output.outputs[0].text)

Online Inference

vllm serve inclusionAI/Ring-mini-linear-2.0-GPTQ-int4 \
              --tensor-parallel-size 1 \
              --pipeline-parallel-size 1 \
              --gpu-memory-utilization 0.90 \
              --max-num-seqs 128 \
              --no-enable-prefix-caching

Evaluation

We evaluate the INT4 and FP8 quantized models using several datasets. The FP8 quantization is applied via the quantization="fp8" argument in SGLang or vLLM.

Ring-mini-linear-2.0

Dataset	BF16	FP8	GPTQ-Int4
AIME25	73.65	72.40	66.56
AIME24	79.95	79.53	74.95
LiveCodeBench	59.53	58.42	56.29
GPQA	65.69	66.79	62.53

Ring-flash-linear-2.0

Dataset	BF16	FP8	GPTQ-Int4
AIME25	85.10	84.22	82.88
LiveCodeBench	69.82	69.44	66.14
GPQA	72.85	72.95	71.72

License

This code repository is licensed under the MIT License.

Citation

If you find our work helpful, feel free to give us a cite.