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Ling-3.0-tiny

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LLMsAI Models & LLMsFreemium · Web

Ling-3.0-tiny is a lightweight hybrid reasoning model with 7.9B parameters, designed for efficient local deployment. It supports fast responses and multi-step reasoning, making it suitable for resource-constrained environments.

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Description

Ling-3.0-tiny is a lightweight hybrid reasoning model developed by inclusionAI, hosted on Hugging Face. It features a total of 7.9 billion parameters, with only 1.3 billion activated per token, making it efficient for local and resource-constrained deployments. The model integrates a 3:1 alternating stacking of Kimi Delta Attention (KDA) and Multi-Head Latent Attention (MLA) layers, along with a sparse Mixture of Experts (MoE) Feed-Forward Network (FFN) comprising 128 routed experts. This architecture allows the model to balance long-context modeling capability, parameter efficiency, and computational cost.

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Ling-3.0-tiny is designed to deliver strong reasoning and agentic capabilities at a low inference cost. It supports both fast responses and multi-step reasoning, with a configurable thinking mode. The model is validated on various hardware, including NVIDIA DGX Spark and Apple Silicon MacBook, enabling capable reasoning and agentic workloads without the need for datacenter-class GPUs. With FP8, it achieves around 100-105 tokens per second on DGX Spark and 86-90 tokens per second on an M4 Pro MacBook, with approximately 8.34 GiB peak memory usage at an 8K context length.

The model's efficiency is highlighted by its performance on the Artificial Analysis Intelligence Index v4.1.1, scoring 25, and the Artificial Analysis Agentic Index, scoring 16. It reaches an output speed of over 160 tokens per second, with approximately 18 seconds of end-to-end latency for a 500-token response. Ling-3.0-tiny is suitable for a wide range of tasks, including agentic tasks, coding, long-context understanding, knowledge reliability, mathematical and scientific reasoning, and instruction following.

  • CategoryAI Models & LLMs
  • Pricing ModelFreemium
  • DeploymentRuns locally
  • PlatformsWeb

Ling-3.0-tiny Highlights

  • 7.9B total parameters

  • 1.3B activated parameters per token

  • Hybrid reasoning capabilities

  • Efficient local deployment

  • 3:1 KDA-MLA architecture

  • Sparse MoE FFN with 128 experts

  • Supports fast responses and multi-step reasoning

  • Validated on NVIDIA DGX Spark and Apple Silicon

Getting Started with Ling-3.0-tiny

  1. Access model page: Visit Hugging Face

  2. Authenticate: Log in to your account

  3. Configure: Set up model parameters

  4. Send prompts: Use the model for inference

  5. Fine-tune: Adjust settings for specific tasks

Ling-3.0-tiny's Use Cases

  • Agentic Tasks
  • Coding
  • Scientific Reasoning
  • Instruction Following
  • Local Deployment

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