InclusionAI/Ring-2.6-1T is now open-sourced
The inclusionAI team has open-sourced Ring-2.6-1T, a trillion-parameter reasoning model designed for complex, real-world task execution. The model supports advanced agent capabilities, adaptive reasoning intensity, and a novel asynchronous reinforcement learning framework. It is available for use via Hugging Face, Docker, SGLang, vLLM, and other deployment methods.
- ▪Ring-2.6-1T is a trillion-parameter AI model focused on real-world complex task execution.
- ▪It features enhanced agent capabilities, allowing it to plan, use tools, and execute multi-step workflows.
- ▪The model supports high and xHigh reasoning effort levels for flexible performance tuning.
- ▪It uses an innovative asynchronous reinforcement learning training method called Async RL with IcePop algorithm.
- ▪Ring-2.6-1T is available under the MIT license and can be deployed using Transformers, vLLM, SGLang, and Docker.
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Story provenance
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Record
| Original publisher | Huggingface |
| Canonical URL | https://huggingface.co/inclusionAI/Ring-2.6-1T |
| Publication time | Sat, 16 May 2026 07:08:23 +0000 |
| Retrieval time | 2026-05-16T07:40:17.734Z |
| Last seen | 2026-05-16T07:40:17.734Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | vtsgusuuSUW0 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
inclusionAI / Ring-2.6-1T like 48 Follow inclusionAI 2.08k Text Generation Transformers Safetensors bailing_hybrid conversational custom_code compressed-tensors License: mit Model card Files Files and versions xet Community 1 Deploy Use this model Instructions to use inclusionAI/Ring-2.6-1T with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers How to use inclusionAI/Ring-2.6-1T with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inclusionAI/Ring-2.6-1T", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages) # Load model directly from transformers import AutoModelForCausalLM model =…
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