inclusionAI/Ling-2.6-1T-base

🤗 Hugging Face | 🤖 ModelScope | Tech Report

Ling-2.6-1T-base

Ling-2.6-1T-base is the base checkpoint behind the Ling-2.6-1T and Ring-2.6-1T. It is a trillion-parameter Mixture-of-Experts language model retrofitted from Ling-2.0-1T-base with a hybrid linear attention design, continued pre-training, and long-context mid-training.

This release is intended for research, continued pre-training, distillation, and supervised or preference-based fine-tuning. It is not a chat-aligned assistant model. If you want an out-of-the-box instruction or reasoning model, use the corresponding Ling-2.6 or Ring-2.6 post-trained checkpoints instead.

1. Model Overview

Ling-2.6-1T-base is designed to preserve the capability of the Ling-2.0 trillion-scale backbone while making long-context training and inference materially more efficient. The core upgrade is a hybrid attention retrofit that combines Lightning Attention with MLA in a 7:1 ratio, together with a smooth migration pipeline from the original GQA-based architecture.

According to the technical report, the model is trained through approximately 9.6T tokens across migration pre-training, continued pre-training, and mid-training, with staged context extension from 4K to 256K. The same base checkpoint is later specialized into:

• Ling-2.6 for instant, token-efficient response
• Ring-2.6 for deeper reasoning and long-horizon agentic workflows

2. Key Features

• Hybrid linear attention architecture combining Lightning Attention and MLA in a 7:1 ratio
• Trillion-parameter MoE backbone upgraded from Ling-2.0-1T-base instead of retraining from scratch
• Long-context training pipeline extended to 256K context during mid-training
• Continued pre-training mixture covering agentic data, long-context data, knowledge-rich web data, math, code, and multilingual corpora
• Strong base-model quality across knowledge, math, code, reasoning, and long-context understanding benchmarks

3. Model Summary

| Item | Value | | --- | --- | | Architecture | Fine-grained MoE with hybrid linear attention | | Parameter Scale | Totoal ~1T, Activated ~63B | | Transformer layers | 80 | | Attention heads | 64 | | Hidden size | 8192 | | Routed experts per MoE layer | 256 | | Shared experts per MoE layer | 1 | | Active routed experts per token | 8 | | Dense FFN layers | First 4 transformer blocks | | Expert intermediate size | 2048 | | Dense intermediate size | 18432 | | Vocabulary size | 157,184 | | Positional encoding | Partial RoPE | | Attention design | Lightning Attention + MLA, 7:1 ratio | | Training recipe | Migration pre-training + continued pre-training + mid-training | | Total training tokens | ~9.6T | | Context training schedule | 4K -> 32K -> 256K |

4. Training Highlights

Architecture Migration

The model starts from Ling-2.0-1T-base and is converted into the Ling-2.6-1T architecture through a multi-stage migration pipeline that includes:

1. Lightning Attention conversion 2. Linear warmup 3. MLA conversion 4. MLA warmup 5. Full continued pre-training

This retrofit is designed to preserve pre-trained capability while reducing long-context compute cost and KV-cache pressure.

Data Mixture

The continued pre-training and mid-training stages include:

• Agentic corpus built from tool-use and coding environments
• Long-context corpus covering mathematics, web parsing, summarization, retrieval, and multi-hop reasoning
• General web knowledge data with targeted STEM and factual augmentation
• Math and code corpora
• Multilingual data spanning 21 languages

5. Base Model Evaluation

The following numbers are selected from the technical report and reflect base-model evaluation rather than chat-aligned or instruction-tuned performance.

| Benchmark | Ling-2.0-1T-base | Ling-2.6-1T-base | | --- | ---: | ---: | | MMLU | 86.03 | 86.82 | | MMLU-Pro | 67.91 | 67.79 | | GPQA | 41.92 | 45.45 | | SimpleQA | 20.87 | 38.26 | | C-SimpleQA | 64.53 | 76.83 | | MMMLU | 68.68 | 71.53 | | GSM8K | 89.31 | 93.93 | | OmniMath | 33.60 | 38.70 | | HumanEval-Plus | 83.54 | 85.98 | | LiveCodeBench | 40.09 | 44.27 | | BIRD-SQL | 42.70 | 44.59 | | BBH | 86.88 | 89.73 | | AutoLogic | 65.76 | 67.43 | | LEval | 72.30 | 76.21 | | LongBenchv2 | 30.02 | 43.54 |

In the technical report, Ling-2.6-1T-base shows broad gains over Ling-2.0-1T-base, especially on factual knowledge, multilingual knowledge coverage, long-context understanding, and reasoning-oriented evaluations, while preserving or improving strong math and code capability. One notable exception in this selected subset is MMLU-Pro, where Ling-2.0-1T-base remains slightly higher.

6. Intended Use

Recommended use cases:

• Continued pre-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
• Single-GPU local inference

7. Limitations

• This is a base model and is not instruction-aligned.
• Outputs may be inaccurate, biased, incomplete, or unsafe without additional post-training.
• Long-context quality depends on the serving stack, positional scaling configuration, and prompt format used at inference time.
• The training mixture includes web-scale and synthetic data, so the model may reproduce factual errors or undesirable artifacts.
• Benchmark results in the technical report are collected under controlled internal evaluation settings and should not be treated as a guarantee of downstream production behavior.

8. Relationship to Other Releases

• Ling-2.6-1T: instruction and instant-response optimized model derived from this base
• Ring-2.6-1T: reasoning- and agent-optimized model derived from the same 2.6 generation

If your goal is interactive assistant use rather than research on base checkpoints, these post-trained models are usually the better starting point.

9. Usage

This is a base checkpoint. One can load it for simple generation or further post-training. Notably, real deployment of a trillion-parameter model typically requires multi-node distributed infrastructure. The example below illustrates the loading pattern only.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name =...