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	---
	license: mit
	---



	<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](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__.

	### High-speed Generation at 300+ token/s

	<p align="center"><img src="https://mdn.alipayobjects.com/huamei_fi95qp/afts/img/bnxIRaK9tzcAAAAAgSAAAAgADkZ7AQFr/original" /></p>

	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.

	<center>

	\| Model \| Context Length \| Download \|
	\|:----------------------:\| :----------------: \|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------:\|
	\| 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) \|
	\| 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) \|
	\| 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) \|
	\| 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) \|
	\| 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) \|
	\| 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) \|

	</center>

	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).


	## Quickstart

	### Convert to safetensors

	Models with safetensors format can be downloaded from [HuggingFace](https://huggingface.co/inclusionAI) or [ModelScope](https://modelscope.cn/organization/inclusionAI).
	If you want to train your model and eval it, you can convert from dcp produced by training.
	```shell
	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`:

	```python
	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:

	```bash
	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:

	```bash
	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:

	```bash
	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:
	```json
	{
	...,
	"rope_scaling": {
	"factor": 4.0,
	"original_max_position_embeddings": 32768,
	"type": "yarn"
	}
	}
	```
	2. 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`](https://docs.vllm.ai/en/latest/).


	### SGLang

	#### Environment Preparation

	We will later submit our model to SGLang official release, now we can prepare the environment following steps:
	```shell
	pip3 install sglang==0.5.2rc0 sgl-kernel==0.3.7.post1
	```
	You can use docker image as well:
	```shell
	docker pull lmsysorg/sglang:v0.5.2rc0-cu126
	```
	Then you should apply patch to sglang installation:
	```shell
	# 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:
	```shell
	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:
	```shell
	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](https://docs.sglang.ai/basic_usage/send_request.html)

	## 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](https://github.com/inclusionAI/Ling-V2/blob/main/docs/gpu_based_training.md) 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](https://github.com/inclusionAI/Ling-V2/blob/main/docs/gpu_based_training.md).

	<center>

	\| Model \| 8 x 80G GPUs (GBS=128) \| 16 x 80G GPUs (GBS=256) \| 32 x 80G GPUs (GBS=512) \|
	\|:-----------------------:\| :--------------------: \| :---------------------: \| :---------------------: \|
	\| LLaMA 3.1 8B (baseline) \| 81222 \| 161319 \| 321403 \|
	\| Qwen3 8B \| 55775 (-31.33%) \| 109799 (-31.94%) \| 219943 (-31.57%) \|
	\| Ling-mini-2.0 \| 109532 (+34.86%) \| 221585 (+37.36%) \| 448726 (+39.61%) \|
	\| Ling-mini-2.0 w/o MTP \| 128298 (+57.96%) \| 307264 (+90.47%) \| 611466 (+90.25%) \|

	</center>

	### Finetuning

	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).

	## License

	This code repository is licensed under [the MIT License](https://github.com/inclusionAI/Ling-V2/blob/master/LICENCE).

	## Citation

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

	```

	```