inclusionAI/Ling-3.0-flash-base-30T

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Introduction

We have open-sourced the Ling-3.0 series, our most efficient language foundation model family to date. To support research and community-driven innovation, we are releasing a collection of checkpoints during the training process as following:

Model	Pre-trained	Mid-trained	Merged (i.e., WSM)
Ling-3.0-tiny	Ling-3.0-tiny-base-30T	Ling-3.0-tiny-base-midtrain	Ling-3.0-tiny-base
Ling-3.0-flash	Ling-3.0-flash-base-30T	Ling-3.0-flash-base-midtrain	Ling-3.0-flash-base

These checkpoints correspond to different stages of the training process:

• Pretrained checkpoint have completed large-scale pretraining but have not undergone mid-training, WSM merging (or learning-rate decay), or post-training.
• Mid-trained checkpoint have completed mid-training but have not undergone WSM merging (or learning-rate decay) or post-training.
• Merged checkpoints have undergone WSM merging (or learning-rate decay) based on the mid-training checkpoints but have not undergone post-training.

These checkpoints are released to support continued pretraining, fine-tuning, and further research. For the post-trained model, please see and see Ling-3.0-tiny and Ling-3.0-flash.

Model Overview

Key features

• Highly sparse (1/64) MoE architecture: 512 routed experts, with only 8 routed experts and 1 shared expert activated per token. This enables broad model capabilities while activating just 5.1B (Non-emb) parameters per token;
• Native hybrid linear attention: Ling-3.0 series adopt a native hybrid linear attention architecture from the very start of pretraining by combining KDA with Gated MLA to enable efficient processing of long-context inputs.
• Warmup-Stable and Merge: We replace conventional learning-rate decay with weighted checkpoint merging. By eliminating the decay phase, our Base Model is better suited for continual pretraining and dynamic data expansion, while enabling offline exploration of different decay profiles without rerunning costly experiments for each strategy.
• Scale Seamlessly: Ling-3.0-tiny-base and Ling-3.0-flash-base share the same training recipe, enabling community to experiment on the Ling-3.0-tiny-base first and then scale validated training strategies to the larger Ling-3.0-flash-base.

Model Type	Base (final checkpoint of pre-training)
Architecture	Hybrid-linear MoE
Parameter Scale	Totoal 124B, Activated 5.1B (Non-emb)
Transformer Layers	35 KDA + 7 Gated MLA (5:1)
Number of Dense Layers	2
Number of Routed Experts	512
Number of Shared Experts	1
Number of Activated Experts	8
Attention Heads	32
Hidden Size	2560
Expert Intermediate Size	768
Dense Intermediate Size	6144
Vocabulary Size	157,184

Base Model Evaluation

To systematically assess the capabilities of the base model, we use a self-built comprehensive benchmark suite covering several key domains, including mathematics, coding, reasoning, multilingual understanding, and long-context comprehension. The performance of the pretrained base checkpoint, i.e., Ling-3.0-flash-base, is compared below:

Domain	Benchmark	Shot Config	Ling-3.0-flash-base	Ling-2.5-flash-base	NVIDIA-Nemotron-3-Super-base	Step-3.5-Flash-base	Hy3-preview-base	MiMo-V2.5-base
			124B A5.1B	104B A7.4B	120B A12B	196B A11B	295B A21B	310B A15B
Knowledge	CCPM(EM)	0-shot	90.00	78.38	80.77	83.90	88.64	88.05
	ARC-C(EM)	0-shot	96.27	95.59	96.27	92.88	94.58	95.59
	AGIEval(Acc)	0-shot	77.50	72.39	67.08	74.27	77.61	71.84
	SimpleQA-Verified(Acc)	5-shot	26.90	15.90	21.20	27.80	24.20	19.10
	MMLU-Pro (EM)	5-shot	67.74	61.36	63.89	63.65	66.44	64.90
	CEval(EM)	5-shot	89.76	87.86	78.12	89.36	90.74	88.60
Code	HumanEval-Plus (Pass@1)	0-shot	81.10	80.49	78.05	78.05	79.27	76.22
	CruxEval(Pass@1)	1-shot	78.50	76.50	72.44	71.69	82.94	70.06
	MultiPL-E(Pass@1)	1-shot	70.79	68.07	57.28	63.21	45.77	53.57
	LiveCodeBench1 (Pass@1)	1-shot	40.09	33.04	38.99	35.68	35.90	35.50
	BigCodeBench(Pass@1)	0-shot	52.19	50.35	25.53	49.74	52.02	50.18
	FullStackBench(Pass@1)	3-shot	51.16	48.43	49.61	50.86	54.95	50.39
	LCBench2 (Pass@1)	3-shot	57.76	57.51	54.22	49.67	46.94	45.12
Math	MATH500(Acc)	4-shot	79.00	74.00	70.80	62.80	60.60	70.00
	OlympiadBench (Acc)	3-shot	47.89	39.31	54.52	34.34	41.27	35.09
	TheoremQA(Acc)	5-shot	61.10	57.95	72.05	56.03	60.96	51.23
	OmniMath (Acc)	3-shot	47.65	36.20	38.55	28.46	33.33	27.03
Reasoning	CommonSenseQA (EM)	5-shot	89.93	87.55	86.24	88.62	86.98	84.60
	BBH (EM)	3-shot	89.17	84.72	89.00	87.19	76.00	86.22
Long-context	LongBench (Acc)	0-shot	52.62	42.81	31.05	21.47	20.58	29.57
	LEval (Acc)	0-shot	83.24	76.89	58.65	60.30	66.29	73.29
Note: 1 LiveCodeBench (2408-2505) 2 LCBench (2301-2502)

Intended Use

Recommended use cases:

• Continued pre-training
• Mid-training
• Supervised fine-tuning for domain adaptation
• Preference optimization and RL post-training Distillation research
• Long-context and MoE systems research

Not recommended as-is for:

• Direct end-user chat deployment
• Safety-critical applications without additional alignment and evaluation
• Production use without post-training and task-specific validation

Usage

For fine-tuning examples, please refer to our ling-cookbook.

FAQ

If you have any question, please feel free to add a discussion.

License

This model is released under the MIT License.