Ling-mini-base-2.0

<p align="center"> <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*4QxcQrBlTiAAAAAAQXAAAAgAemJ7AQ/original" width="100"/> <p> <p align="center">🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope</a></p>

Introduction

Today, we are excited to announce the open-sourcing of Ling 2.0 — a family of MoE-based large language models that combine SOTA performance with high efficiency. The first released version, Ling-mini-2.0, is compact yet powerful. It has 16B total parameters, but only 1.4B are activated per input token (non-embedding 789M). Trained on more than 20T tokens of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-mini-2.0 achieves remarkable improvements in complex reasoning and instruction following. With just 1.4B activated parameters, it still reaches the top-tier level of sub-10B dense LLMs and even matches or surpasses much larger MoE models.

<p align="center"><img src="https://mdn.alipayobjects.com/huamei_fi95qp/afts/img/2NKZS5LVXzcAAAAASBAAAAgADkZ7AQFr/fmt.webp" /></p>

Strong General and Professional Reasoning

We evaluated Ling-mini-2.0 on challenging general reasoning tasks in coding (LiveCodeBench, CodeForces) and mathematics (AIME 2025, HMMT 2025), as well as knowledge-intensive reasoning tasks across multiple domains (MMLU-Pro, Humanity's Last Exam). Compared with sub-10B dense models (e.g., Qwen3-4B-instruct-2507, Qwen3-8B-nothinking) and larger-scale MoE models (Ernie-4.5-21B-A3B-PT, GPT-OSS-20B/low), Ling-mini-2.0 demonstrated outstanding overall reasoning capabilities.

7× Equivalent Dense Performance Leverage

Guided by Ling Scaling Laws, Ling 2.0 adopts a 1/32 activation ratio MoE architecture, with empirically optimized design choices in expert granularity, shared expert ratio, attention ratio, aux-loss free + sigmoid routing strategy, MTP loss, QK-Norm, half RoPE, and more. This enables small-activation MoE models to achieve over 7× equivalent dense performance. In other words, Ling-mini-2.0 with only 1.4B activated parameters (non-embedding 789M) can deliver performance equivalent to a 7–8B dense model.

High-speed Generation at 300+ token/s

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The highly sparse small-activation MoE architecture also delivers significant training and inference efficiency. In simple QA scenarios (within 2000 tokens), Ling-mini-2.0 generates at 300+ token/s (on H20 deployment) — more than 2× faster than an 8B dense model. Ling-mini-2.0 is able to handle 128K context length with YaRN, as sequence length increases, the relative speedup can reach over 7×.

<p align="center"><img src="https://raw.githubusercontent.com/inclusionAI/Ling-V2/refs/heads/main/figures/needle_in_a_haystack.webp" /></p>

Open-sourced FP8 Efficient Training Solution

Ling 2.0 employs FP8 mixed-precision training throughout. Compared with BF16, experiments with over 1T training tokens show nearly identical loss curves and downstream benchmark performance. To support the community in efficient continued pretraining and fine-tuning under limited compute, we are also open-sourcing our FP8 training solution. Based on tile/blockwise FP8 scaling, it further introduces FP8 optimizer, FP8 on-demand transpose weight, and FP8 padding routing map for extreme memory optimization. On 8/16/32 80G GPUs, compared with LLaMA 3.1 8B and Qwen3 8B, Ling-mini-2.0 achieved 30–60% throughput gains with MTP enabled, and 90–120% throughput gains with MTP disabled.

A More Open Opensource Strategy

We believe Ling-mini-2.0 is an ideal starting point for MoE research. For the first time at this scale, it integrates 1/32 sparsity, MTP layers, and FP8 training — achieving both strong effectiveness and efficient training/inference performance, making it a prime candidate for the small-size LLM segment. To further foster community research, in addition to releasing the post-trained version, we are also open-sourcing five pretraining checkpoints: the pre-finetuning Ling-mini-2.0-base, along with four base models trained on 5T, 10T, 15T, and 20T tokens, enabling deeper research and broader applications.

Model Downloads

You can download the following table to see the various stage of Ling-mini-2.0 models(1.43B activated of 16.26B total params). If you are located in mainland China, we also provide the model on ModelScope.cn to speed up the download process.

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Note: If you are interested in previous version, please visit the past model collections in Huggingface or ModelScope.

Quickstart

Convert to safetensors

Models with safetensors format can be downloaded from HuggingFace or ModelScope. If you want to train your model and eval it, you can convert from dcp produced by training.

python tools/convert_dcp_to_safe_tensors.py --checkpoint-path ${DCP_PATH} --target-path ${SAFETENSORS_PATH}

Currently, BF16 and FP8 formats are supported, you can use convert parameter to handle it:

• --force-bf16 for BF16 format.
• --force-fp8 for FP8 format.

🤗 Hugging Face Transformers

Here is a code snippet to show you how to use the chat model with transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "inclusionAI/Ling-mini-2.0"

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

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt", return_token_type_ids=False).to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

🤖 ModelScope

If you're in mainland China, we strongly recommend you to use our model from 🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope</a>.

Deployment

vLLM

vLLM supports offline batched inference or launching an OpenAI-Compatible API Service for online inference.

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:

git clone -b v0.10.0 https://github.com/vllm-project/vllm.git
cd vllm
wget https://raw.githubusercontent.com/inclusionAI/Ling-V2/refs/heads/main/inference/vllm/bailing_moe_v2.patch
git apply bailing_moe_v2.patch
pip install -e .

Offline Inference:

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ling-mini-2.0")

sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=16384)

llm = LLM(model="inclusionAI/Ling-mini-2.0", dtype='bfloat16')
prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
outputs = llm.generate([text], sampling_params)

Online Inference:

vllm serve inclusionAI/Ling-mini-2.0 \
              --tensor-parallel-size 2 \
              --pipeline-parallel-size 1 \
              --use-v2-block-manager \
              --gpu-memory-utilization 0.90

To handle long context in vLLM using YaRN, we need to follow these two steps:

1. Add a rope_scaling field to the model's config.json file, for example:

{
  ...,
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  }
}

1. Use an additional parameter --max-model-len to specify the desired maximum context length when starting the vLLM service.

For detailed guidance, please refer to the vLLM instructions.

SGLang

Environment Preparation

We will later submit our model to SGLang official release, now we can prepare the environment following steps:

pip3 install sglang==0.5.2rc0 sgl-kernel==0.3.7.post1

You can use docker image as well:

docker pull lmsysorg/sglang:v0.5.2rc0-cu126

Then you should apply patch to sglang installation:

# patch command is needed, run `yum install -y patch` if needed
patch -d `python -c 'import sglang;import os; print(os.path.dirname(sglang.__file__))'` -p3 < inference/sglang/bailing_moe_v2.patch

Run Inference

BF16 and FP8 models are supported by SGLang now, it depends on the dtype of the model in ${MODEL_PATH}. They both share the same command in the following:

• Start server:

python -m sglang.launch_server \
    --model-path $MODLE_PATH \
    --host 0.0.0.0 --port $PORT \
    --trust-remote-code \
    --attention-backend fa3

MTP is supported for base model, and not yet for chat model. You can add parameter --speculative-algorithm NEXTN to start command.

• Client:

curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "auto", "messages": [{"role": "user", "content": "What is the capital of France?"}]}'
"""

More usage can be found here

Training

We also provide a complete and efficient training framework that covers both pre-training and finetune. Based on this framework, continue training can be performed on the Ling-mini-2.0 checkpoint. With our training framework, the training throughput of the Ling-mini-2.0 model is significantly better than that of the existing Dense 8B model (Qwen3-8B, Llama3-8B).

Pre-training

Pretraining demo to Continue pretraining Ling models.

Performance Benchmark

The table below shows the pre-training performance of several models, measured in tokens per second on 8, 16, and 32 80G GPUs. Ling-mini-2.0 achieves significantly higher training efficiency compared to the baseline, making it easier and more cost-effective to continue pre-training with our demo scripts.

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Finetuning

We recommend you to use Llama-Factory to finetune Ling. In addition to that, you can also use Megatron for finetuning.

License

This code repository is licensed under the MIT License.

Citation

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