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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 = "inclusionAI/Ling-2.6-1T-base"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
        model_name,
        trust_remote_code=True,
        device_map="auto",
)

prompt = "Explain the difference between full attention and linear attention."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For production inference, prefer distributed serving stacks such as SGLang or other engines that support the released architecture and model size. You can refer to our instruction version for SGLang deployment.

10. License

This checkpoint and code repository is licensed under the MIT License.