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# Ling-2.6-flash-base Ling-2.6-flash-base is the base checkpoint behind the Ling-2.6-flash model. It is a flash-scale Mixture-of-Experts language model retrofitted from the Ling-2.0 base checkpoint 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 model, use the corresponding post-trained Ling-2.6-flash checkpoint instead. ## 1. Model Overview Ling-2.6-flash-base is designed for efficient instant-response modeling with stronger long-context efficiency than the previous GQA-based Ling-2.0 generation. 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 architecture. Ling-2.6 base models are trained through approximately 9.6T tokens across migration pre-training, continued pre-training, and mid-training, with staged context extension from 4K to 256K. Ling-2.6-flash-base serves as the base checkpoint for the post-trained Ling-2.6-flash instant model. ## 2. Key Features - Hybrid linear attention architecture combining Lightning Attention and MLA in a 7:1 ratio - Flash-scale MoE backbone optimized for efficient serving and high token efficiency - 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 | Total ~104B, Activated ~7.4B | | Transformer layers | 32 | | Routed experts per MoE layer | 256 | | Shared experts per MoE layer | 1 | | Active routed experts per token | 8 | | Attention heads | 32 | | Dense FFN layers | 1 | | Hidden size | 4096 | | Dense intermediate size | 9216 | | Expert intermediate size | 1024 | | KV LoRA rank | 512 | | Q LoRA rank | 1536 | | Layer group size | 8 | | 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 is converted from the Ling-2.0 generation into the Ling-2.6-flash 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, KV-cache pressure, and decode latency. ### 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-flash-base | Ling-2.6-flash-base | | --- | ---: | ---: | | MMLU | 82.98 | 84.13 | | MMLU-Pro | 60.73 | 61.36 | | GPQA | 35.35 | 37.88 | | SimpleQA | 10.01 | 18.33 | | C-SimpleQA | 49.43 | 63.53 | | MMMLU | 62.76 | 64.76 | | GSM8K | 90.60 | 91.89 | | OmniMath | 28.30 | 29.90 | | HumanEval-Plus | 83.54 | 81.10 | | LiveCodeBench | 30.40 | 33.48 | | BIRD-SQL | 38.69 | 38.40 | | BBH | 84.82 | 85.06 | | AutoLogic | 61.10 | 62.82 | | LEval | 73.41 | 77.86 | | LongBenchv2 | 33.40 | 34.19 | Ling-2.6-flash-base shows broad gains over Ling-2.0-flash-base, especially on knowledge-oriented, reasoning-oriented, and long-context evaluations. ## 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 - Production use without post-training and task-specific validation ## 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-flash](https://huggingface.co/inclusionAI/Ling-2.6-flash): instruction and instant-response optimized model derived from this base. If your goal is interactive assistant use rather than research on base checkpoints, the post-trained Ling-2.6-flash model is usually the better starting point. ## 9. Usage This is a base checkpoint. The example below illustrates the loading pattern only. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "inclusionAI/Ling-2.6-flash-base" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) prompt = "Summarize the benefits of hybrid 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 serving stacks that support the released architecture and remote code path. ## 10. License This model is released under the MIT License.