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Model: inclusionAI/Ling-mini-base-2.0-5T Source: Original Platform
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README.md
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license: mit
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---
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<p align="center">
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<img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*4QxcQrBlTiAAAAAAQXAAAAgAemJ7AQ/original" width="100"/>
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<p>
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<p align="center">🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope</a></p>
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## Introduction
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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__.
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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.
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<p align="center"><img src="https://mdn.alipayobjects.com/huamei_fi95qp/afts/img/2NKZS5LVXzcAAAAASBAAAAgADkZ7AQFr/fmt.webp" /></p>
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### Strong General and Professional Reasoning
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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.
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### 7× Equivalent Dense Performance Leverage
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Guided by [Ling Scaling Laws](https://arxiv.org/abs/2507.17702), 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__.
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### High-speed Generation at 300+ token/s
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<p align="center"><img src="https://mdn.alipayobjects.com/huamei_fi95qp/afts/img/bnxIRaK9tzcAAAAAgSAAAAgADkZ7AQFr/original" /></p>
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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×__.
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<p align="center"><img src="https://raw.githubusercontent.com/inclusionAI/Ling-V2/refs/heads/main/figures/needle_in_a_haystack.webp" /></p>
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### Open-sourced FP8 Efficient Training Solution
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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__.
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### A More Open Opensource Strategy
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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.
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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.
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## Model Downloads
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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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<center>
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| **Model** | **Context Length** | **Download** |
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|:----------------------:| :----------------: |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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| Ling-mini-base-2.0 | 32K -> 128K (YaRN) | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0) |
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| Ling-mini-base-2.0-5T | 4K | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-5T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-5T) |
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| Ling-mini-base-2.0-10T | 4K | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-10T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-10T) |
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| Ling-mini-base-2.0-15T | 4K | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-15T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-15T) |
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| Ling-mini-base-2.0-20T | 4K | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-20T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-20T) |
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| Ling-mini-2.0 | 32K -> 128K (YaRN) | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-2.0) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-2.0) |
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</center>
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Note: If you are interested in previous version, please visit the past model collections in [Huggingface](https://huggingface.co/inclusionAI) or [ModelScope](https://modelscope.cn/organization/inclusionAI).
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## Quickstart
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### Convert to safetensors
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Models with safetensors format can be downloaded from [HuggingFace](https://huggingface.co/inclusionAI) or [ModelScope](https://modelscope.cn/organization/inclusionAI).
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If you want to train your model and eval it, you can convert from dcp produced by training.
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```shell
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python tools/convert_dcp_to_safe_tensors.py --checkpoint-path ${DCP_PATH} --target-path ${SAFETENSORS_PATH}
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```
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Currently, BF16 and FP8 formats are supported, you can use convert parameter to handle it:
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- `--force-bf16` for BF16 format.
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- `--force-fp8` for FP8 format.
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### 🤗 Hugging Face Transformers
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Here is a code snippet to show you how to use the chat model with `transformers`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "inclusionAI/Ling-mini-2.0"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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dtype="auto",
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device_map="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Give me a short introduction to large language models."
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messages = [
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{"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt", return_token_type_ids=False).to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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### 🤖 ModelScope
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If you're in mainland China, we strongly recommend you to use our model from 🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope</a>.
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## Deployment
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### vLLM
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vLLM supports offline batched inference or launching an OpenAI-Compatible API Service for online inference.
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#### Environment Preparation
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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:
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```bash
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git clone -b v0.10.0 https://github.com/vllm-project/vllm.git
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cd vllm
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wget https://raw.githubusercontent.com/inclusionAI/Ling-V2/refs/heads/main/inference/vllm/bailing_moe_v2.patch
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git apply bailing_moe_v2.patch
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pip install -e .
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```
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#### Offline Inference:
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```bash
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ling-mini-2.0")
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sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=16384)
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llm = LLM(model="inclusionAI/Ling-mini-2.0", dtype='bfloat16')
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prompt = "Give me a short introduction to large language models."
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messages = [
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{"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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outputs = llm.generate([text], sampling_params)
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```
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#### Online Inference:
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```bash
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vllm serve inclusionAI/Ling-mini-2.0 \
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--tensor-parallel-size 2 \
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--pipeline-parallel-size 1 \
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--use-v2-block-manager \
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--gpu-memory-utilization 0.90
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```
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To handle long context in vLLM using YaRN, we need to follow these two steps:
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1. Add a `rope_scaling` field to the model's `config.json` file, for example:
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```json
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{
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...,
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"rope_scaling": {
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"factor": 4.0,
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"original_max_position_embeddings": 32768,
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"type": "yarn"
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}
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}
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```
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2. Use an additional parameter `--max-model-len` to specify the desired maximum context length when starting the vLLM service.
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For detailed guidance, please refer to the vLLM [`instructions`](https://docs.vllm.ai/en/latest/).
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### SGLang
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#### Environment Preparation
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We will later submit our model to SGLang official release, now we can prepare the environment following steps:
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```shell
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pip3 install sglang==0.5.2rc0 sgl-kernel==0.3.7.post1
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```
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You can use docker image as well:
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```shell
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docker pull lmsysorg/sglang:v0.5.2rc0-cu126
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```
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Then you should apply patch to sglang installation:
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```shell
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# patch command is needed, run `yum install -y patch` if needed
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patch -d `python -c 'import sglang;import os; print(os.path.dirname(sglang.__file__))'` -p3 < inference/sglang/bailing_moe_v2.patch
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```
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#### Run Inference
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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:
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- Start server:
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```shell
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python -m sglang.launch_server \
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--model-path $MODLE_PATH \
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--host 0.0.0.0 --port $PORT \
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--trust-remote-code \
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--attention-backend fa3
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```
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MTP is supported for base model, and not yet for chat model. You can add parameter `--speculative-algorithm NEXTN`
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to start command.
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- Client:
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```shell
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curl -s http://localhost:${PORT}/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{"model": "auto", "messages": [{"role": "user", "content": "What is the capital of France?"}]}'
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"""
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```
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More usage can be found [here](https://docs.sglang.ai/basic_usage/send_request.html)
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## Training
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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).
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### Pre-training
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[Pretraining demo](https://github.com/inclusionAI/Ling-V2/blob/main/docs/gpu_based_training.md) to Continue pretraining Ling models.
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#### Performance Benchmark
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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](https://github.com/inclusionAI/Ling-V2/blob/main/docs/gpu_based_training.md).
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<center>
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| **Model** | **8 x 80G GPUs (GBS=128)** | **16 x 80G GPUs (GBS=256)** | **32 x 80G GPUs (GBS=512)** |
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|:-----------------------:| :--------------------: | :---------------------: | :---------------------: |
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| LLaMA 3.1 8B (baseline) | 81222 | 161319 | 321403 |
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| Qwen3 8B | 55775 (-31.33%) | 109799 (-31.94%) | 219943 (-31.57%) |
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| Ling-mini-2.0 | 109532 (+34.86%) | 221585 (+37.36%) | 448726 (+39.61%) |
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| Ling-mini-2.0 w/o MTP | 128298 (+57.96%) | 307264 (+90.47%) | 611466 (+90.25%) |
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</center>
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### Finetuning
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We recommend you to use [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory) to [finetune Ling](https://github.com/inclusionAI/Ling-V2/blob/main/docs/llamafactory_finetuning.md). In addition to that, you can also use [Megatron for finetuning](https://github.com/inclusionAI/Ling-V2/blob/main/docs/megatron_sft_training.md).
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## License
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This code repository is licensed under [the MIT License](https://github.com/inclusionAI/Ling-V2/blob/master/LICENCE).
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## Citation
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If you find our work helpful, feel free to give us a cite.
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```
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```
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