Ling-3.0-flash-fp4
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Ling-3.0-flash-fp8
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View details ↗Ling-3.0-flash-GGUF
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View details ↗Ling-3.0-flash-int4
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View details ↗Ling-3.0-flash-UBTree
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View details ↗Ling-3.0-flash-VL-fp4
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View details ↗Ling-3.0-flash-VL-fp8
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View details ↗Ling-3.0-flash-VL-int4
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View details ↗Ling-3.0-tiny
inclusionAI🤗 Hugging Face | 🤖 ModelScope | 🐙 OpenRouter We are introducing **Ling-3.0-tiny**, a lightweight hybrid reasoning MoE model with **7.9B** total parameters and only **1.3B** activated parameters per token. It is designed to deliver strong reasoning and agentic capabilities at low inference cost, making advanced model capabilities more accessible for local and resource-constrained deployment. BF16, FP8, and INT4 weights are provided for a wide range of hardware and deployment settings. + **Efficient Hybrid-Linear Architecture:** Ling-3.0-tiny integrates a 3:1 alternating stacking of KDA and MLA (3 Kimi Delta Attention layers followed by 1 Multi-Head Latent Attention layer per 4-layer block) with a sparse MoE FFN comprising 128 routed experts. Each token activates only 8 routed experts and 1 shared expert, allowing the model to balance long-context modeling capability, parameter efficiency, and computational cost. + **Native Hybrid Reasoning and Agentic Capabilities:** Ling-3.0-tiny supports both fast responses and multi-step reasoning, with thinking mode configurable per request through `enable_thinking`. It delivers balanced performance across general agent tasks, coding, mathematical and scientific reasoning, and instruction following. + **Local and Edge Deployment:** Designed for efficient local deployment, Ling-3.0-tiny has been validated on **NVIDIA DGX Spark, Apple Silicon MacBook, and Mac mini**, enabling capable reasoning and agentic workloads without datacenter-class GPUs. With FP8, Ling-3.0-tiny reaches around **100-105 tokens/s on DGX Spark** and **86-90 tokens/s on an M4 Pro MacBook**, with approximately **8.34 GiB peak memory usage** at an 8K context length.
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View details ↗Ling-3.0-tiny-base
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View details ↗Ling-3.0-tiny-base-30T
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View details ↗Ling-3.0-tiny-base-midtrain
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View details ↗Ling-3.0-tiny-fp8
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View details ↗Ling-3.0-tiny-GGUF
inclusionAI🤗 Hugging Face | 🤖 ModelScope | 🐙 OpenRouter Ling-3.0-tiny is a lightweight hybrid reasoning MoE model with **7.9B** total parameters and only **1.3B** activated parameters per token. It is designed to deliver strong reasoning and agentic capabilities at low inference cost, making advanced model capabilities more accessible for local and resource-constrained deployment. Find more details in the original model card: https://huggingface.co/inclusionAI/Ling-3.0-tiny
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View details ↗Ling-3.0-tiny-int4
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View details ↗Ling-Coder-lite
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View details ↗Ling-flash-2.0
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View details ↗Ling-lite
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View details ↗Ling-lite-1.5
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View details ↗Ling-lite-1.5-2507
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View details ↗Ling-lite-base-1.5
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View details ↗Ling-mini-2.0
inclusionAIToday, 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. 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. 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**.
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View details ↗Ling-mini-2.0-GGUF
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View details ↗Ling-mini-base-2.0
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