Ring-mini-linear-2.0 / README.md

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metadata

license: mit
language:
  - en
base_model:
  - inclusionAI/Ling-mini-base-2.0-20T
pipeline_tag: text-generation
library_name: transformers
tags:
  - moe

Ring-mini-linear-2.0

🤗 Hugging Face | 🤖 ModelScope

Introduction

We are excited to announce the official open-source release of Ring-mini-linear-2.0!

Building on the success of our Ling 2.0 series, this model continues to leverage a powerful hybrid architecture of linear and standard attention, perfectly balancing high performance with superior efficiency. By integrating our proven MoE design with optimizations like a 1/32 expert activation ratio and MTP layers, Ring-mini-linear achieves the performance of a 8 B dense model while activating only 1.4 B parameters. This model was converted from Ling-mini-base-2.0, further trained on an additional 600 B tokens. When it comes to benchmarks, Ring-mini-linear-2.0 not only holds its own against standard attention models (like ring-mini-2) but also outperforms other open-source MoE and Dense models in its class on several demanding tasks. Plus, with native support for a 128k long context, it's faster and more precise than ever, especially when handling long-form inputs and outputs.

Figure 1: Hybrid Linear Model Architecture

Evaluation

To properly evaluate the model's reasoning capabilities, we compared it against 3 other models—Ring-mini-2.0, Qwen3-8B-thinking, and GPT-OSS-20B-Medium—on 6 challenging reasoning benchmarks spanning mathematics, coding, and science. The results demonstrate that the performance of the hybrid linear architecture is by no means inferior to that of standard softmax attention; in fact, it even outperforms the other models on 3 of the benchmarks.

Figure 2: Model Performance Comparison

Here is a demo of a small Snake game, with the code generated by our model.

Figure 3: Snake Game

Linear Attention, Highly Sparse，High-Speed Generation

Thanks to its hybrid attention mechanism and highly sparse MoE architecture, Ring-mini-linear-2.0 achieves near-linear time complexity and constant space complexity, resulting in outstanding inference efficiency. To fully demonstrate this advantage, we conducted a head-to-head comparison between our model and top-tier competitors of similar size or performance. The results are remarkable. In the prefill stage, Ring-mini-linear-2.0's performance is exceptional; when the context length exceeds 256k, its throughput is over 12 times higher than that of Qwen3-8B. Furthermore, in the high-concurrency decode stage, its capabilities are even more pronounced. For generation lengths exceeding 32k, its throughput easily surpasses 12 times that of Qwen3-8B.

Figure 4: Ring-mini-linear-2.0 prefill throughput

Figure 5: Ring-mini-linear-2.0 decode throughput

Model Downloads

Model	#Total Params	#Activated Params	Context Length	Download
Ring-mini-linear-2.0	16.8B	1.4B	128K	🤗 HuggingFace 🤖 Modelscope

Quickstart

Requirements

pip install flash-linear-attention==0.3.2
pip install transformers==4.56.1

🤗 Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "inclusionAI/Ring-mini-linear-2.0"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)


prompts = [
    "Give me a short introduction to large language models."
]
input_texts = []
for prompt in prompts:
    messages = [
        {"role": "user", "content": prompt}
    ]
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
        enable_thinking=True
    )
    input_texts.append(text)

print(input_texts)

model_inputs = tokenizer(input_texts, return_tensors="pt", return_token_type_ids=False, padding=True, padding_side='left').to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=8192,
    do_sample=False,
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

responses = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)

print("*" * 30)
print(responses)
print("*" * 30)

SGLang

python -m sglang.launch_server \
    --model-path <model_path> \
    --trust-remote-code \
    --tp-size 1 \
    --disable-radix-cache \
    --json-model-override-args "{\"linear_backend\": \"seg_la\"}"

inclusionAI
/

Ring-mini-linear-2.0