{ "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "tool": { "slug": "wevisone-vl-2b", "name": "WeVisOne-VL-2B", "vendor": "Tencent (WeChat Vision)", "vendorUrl": "https://www.tencent.com", "kind": "weights", "category": "open-weights", "summary": "WeVisOne-VL-2B is an open-weight perception model of 2.4B parameters from Tencent's WeChat Vision team, built on Qwen3-VL. Name something in an image and it returns boxes, points, counts, text, masks or alpha mattes. Apache 2.0.", "url": "https://www.anchorterminal.com/tools/wevisone-vl-2b", "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json", "dns": "wevisone-vl-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/WeVisOne-VL-2B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/WeVisOne-VL-2B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware, 4.88 GB in BF16 (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/Tencent/WeVisOne-VL", "capabilities": [ "model.open-weights", "docs.ocr" ], "tags": [ "open-weights", "model", "vision", "grounding", "object-detection", "segmentation", "matting", "ocr", "counting", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-10-28", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 65, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "breakdown": [ { "key": "capability", "name": "Capability", "weight": 35, "effectiveWeight": 0, "pending": true, "points": 0, "reason": "Pending for this listing. Nobody independent has published results for it in at least two areas yet, so its weight is shared across the other four and the total is capped at 61 until they do or our own runs start." }, { "key": "reach", "name": "Reach", "weight": 25, "effectiveWeight": 38.46, "score": 70, "points": 26.92, "reason": "Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.", "checked": "2026-11-10" }, { "key": "risk", "name": "Risk", "weight": 20, "effectiveWeight": 30.77, "score": 55, "points": 16.92, "reason": "File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.", "checked": "2026-11-10" }, { "key": "interface", "name": "Agent interface", "weight": 10, "effectiveWeight": 15.38, "score": 70, "points": 10.77, "reason": "Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.", "checked": "2026-11-10" }, { "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "effectiveWeight": 15.38, "score": 48, "points": 7.38, "reason": "Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.", "checked": "2026-11-10" } ], "assessment": { "date": "2026-11-10", "basis": "public evidence", "confidence": "low", "notes": { "capability": "Pending for this listing. Independent results were found in 0 areas (none), and Capability needs 2. Its weight is shared across the other four categories and the total is capped at 61 until there are more, or our own runs start.", "interface": "Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.", "reach": "Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.", "risk": "File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.", "upkeep": "Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48." }, "sources": [ { "what": "model card", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B", "seen": "2026-11-10" }, { "what": "gating, dates, parameter count, downloads, likes, file list and hashes", "url": "https://huggingface.co/api/models/tencent/WeVisOne-VL-2B?blobs=true", "seen": "2026-11-10" }, { "what": "inference providers, none mapped", "url": "https://huggingface.co/api/models/tencent/WeVisOne-VL-2B?expand[]=inferenceProviderMapping", "seen": "2026-11-10" }, { "what": "commit history", "url": "https://huggingface.co/api/models/tencent/WeVisOne-VL-2B/commits/main", "seen": "2026-11-10" }, { "what": "architecture and positions", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B/raw/main/config.json", "seen": "2026-11-10" }, { "what": "chat template", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B/raw/main/chat_template.jinja", "seen": "2026-11-10" }, { "what": "discussions, replies counted by script from authors, dates and member flags", "url": "https://huggingface.co/api/models/tencent/WeVisOne-VL-2B/discussions", "seen": "2026-11-10" }, { "what": "Tencent's GitHub README for the model: tasks, outputs, backends, data engine", "url": "https://raw.githubusercontent.com/Tencent/WeVisOne-VL/main/README.md", "seen": "2026-11-10" }, { "what": "vLLM v0.33.0 registry, Qwen3VLForConditionalGeneration", "url": "https://raw.githubusercontent.com/vllm-project/vllm/v0.33.0/vllm/model_executor/models/registry.py", "seen": "2026-11-10" }, { "what": "SGLang v0.5.23 qwen3_vl (shallow clone)", "url": "https://raw.githubusercontent.com/sgl-project/sglang/v0.5.23/python/sglang/srt/models/qwen3_vl.py", "seen": "2026-11-10" }, { "what": "Transformers v5.22.2 qwen3_vl", "url": "https://github.com/huggingface/transformers/tree/v5.22.2/src/transformers/models/qwen3_vl", "seen": "2026-11-10" }, { "what": "our other Tencent listings, for company facts", "url": "https://www.anchorterminal.com/tools/hy3", "seen": "2026-11-10" } ], "openQuestions": [ "It's a perception model listed among chat models, since there's no category for vision models that return boxes and masks", "Runtimes credits vLLM, SGLang and Transformers for the backbone. The pixel-level tasks run only through Tencent's package, which a stricter reading wouldn't credit", "File safety is 15 for a pickle beside the weights. The package that loads it lives on GitHub, not in the model repository, so the 10 for repository code wasn't taken off", "Terms is 15 on the metadata and the LICENSE file. The card's own sentence speaks of code", "Capability is pending because no independent results were looked up on the day", "unchecked on 10 November: the paper (arXiv 2610.31329), the cookbooks, the package's code, the evaluation README, GitHub issues, and Tencent's security pages, read for the Hy3 listing" ] }, "negative": 0, "verdict": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.", "bestFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "strengths": [ "Seven perception tasks in one model, from boxes to alpha mattes", "Returns None for a target that isn't in the image", "Apache 2.0, ungated", "Each task's input and output is documented", "4.88 GB in BF16, so it fits a 8 GB machine" ], "weaknesses": [ "The pixel decoder ships as a pickle file", "Masks and mattes need Tencent's package from GitHub", "No quantised build from Tencent", "One image and one turn at a time, no video", "No reply from Tencent on the repository's discussions", "No context length, languages, safety results or data sources on the card" ], "agentNotes": [ "Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`", "Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting", "A None answer means the target isn't in the image. Treat it as a result, not an error", "mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's", "Send one image a turn. Video grids are rejected by the decoder", "The card says it isn't for identifying individuals or for high-impact decisions on its own" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "provenanceScore": 68, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 }, "scoreHistory": [ { "date": "2026-11-10", "grade": "C", "kind": "listed", "method": "0.7", "score": 61 } ] }, "connect": { "install": "git clone https://github.com/Tencent/WeVisOne-VL.git \u0026\u0026 cd WeVisOne-VL\npip install -r requirements.txt \u0026\u0026 pip install -e .\n# then, in Python: WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"transformers\") or backend=\"vllm\"", "http": "# No HTTP API of its own. Tencent's package calls model.predict(image, task=\"detection\", query=[...]).\n# For boxes and points only, vLLM serves the backbone at http://localhost:8000/v1 with: vllm serve tencent/WeVisOne-VL-2B" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/wevisone-vl-2b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "hy-memory-30b-a3b", "hy3", "wevisone-vl-4b" ], "weights": { "trainedBy": "Tencent WeChat Vision", "params": "2.4B", "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 8 GB machine.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "WeVisOne-VL-2B (BF16 safetensors)", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 4.88, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build. Masks and mattes also need mask_decoder.pt and Tencent's package" } ], "run": "vllm serve tencent/WeVisOne-VL-2B", "runBuild": "WeVisOne-VL-2B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "WeVisOne-VL", "url": "https://www.anchorterminal.com/open-weights/#tencent", "others": [ { "name": "WeVisOne-VL-4B", "url": "https://huggingface.co/tencent/WeVisOne-VL-4B", "params": "4.8B (4,830,581,248 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/wevisone-vl-4b" } ], "olderCount": 22 }, "notable": [ "14,245 downloads in the last 30 days and 83 likes on Hugging Face on 10 November 2026 (https://huggingface.co/api/models/tencent/WeVisOne-VL-2B)", "One model does detection, referring localisation, pointing, counting, OCR with boxes, segmentation and alpha matting, chosen by a task name and a query (https://github.com/Tencent/WeVisOne-VL)", "It answers None when what you asked for isn't in the image, which the README calls reliable rejection (https://github.com/Tencent/WeVisOne-VL)", "Masks and mattes come from a 9.6M-parameter decoder shipped as mask_decoder.pt, a pickle file that Tencent's own package loads (https://huggingface.co/tencent/WeVisOne-VL-2B/tree/main)", "The card says the release is for research, and not for autonomous high-impact decisions or identifying individuals (https://huggingface.co/tencent/WeVisOne-VL-2B)", "The one question on the repository has a reply from a Tencent member (https://huggingface.co/api/models/tencent/WeVisOne-VL-2B/discussions)" ], "area": "models", "details": [ { "label": "Surface graded", "value": "The open weights at huggingface.co/tencent/WeVisOne-VL-2B, read from the model card, the files and their metadata, the config, the chat template, the commit history, the README of Tencent's GitHub repository for the model and the runtimes' own code at their latest releases on 10 November 2026. Tencent's company facts come from our other Tencent listings. The paper, the cookbooks and the package's code weren't read. Nothing was downloaded or run" }, { "label": "Model", "value": "2,441,744,384 parameters in the safetensors file, plus a 9,609,764-parameter pixel decoder in mask_decoder.pt. Qwen3VLForConditionalGeneration with a 28-layer language model, built on Qwen3-VL by WeChat Vision at Tencent" }, { "label": "Files", "value": "One safetensors file in BF16 (4.88 GB), mask_decoder.pt (0.04 GB, a PyTorch pickle), chat_template.jinja, config, processor and tokenizer files and the LICENSE. No Python. Hugging Face lists a SHA-256 for the large files" }, { "label": "Licence", "value": "Apache 2.0 in the card's metadata, with the LICENSE file in the repository. The card's licence section speaks of Tencent's code and says third-party components keep their own licences" }, { "label": "Runtimes", "value": "Tencent documents its own Python package with a Transformers backend and a vLLM backend. Released Transformers 5.22.2, vLLM 0.33.0 and SGLang 0.5.23 carry the Qwen3-VL architecture, which gives the text output: boxes, points, counts and OCR. Masks and mattes need the pixel decoder, so Tencent's package" }, { "label": "Context", "value": "Not stated by Tencent. config.json carries Qwen3-VL's 262,144 positions and the tokenizer config says 8,192. It reads one image a turn, at up to 1,960 visual tokens in the README's results" }, { "label": "Hardware", "value": "No memory figure on the card. The files are 4.92 GB in BF16. The package takes a device of auto, cuda, npu or cpu" }, { "label": "Training data", "value": "The README describes a data engine that finds concepts from a knowledge graph, localises them with expert models checked by a vision-language model, and extends boxes to masks and mattes, with over 100 million images annotated. No sources named and no cut-off" }, { "label": "Tasks", "value": "Open-vocabulary detection, referring localisation, pointing, counting by box or by point, OCR and finding given text, referring segmentation and promptable alpha matting. One image and one turn at a time" }, { "label": "Languages", "value": "Not stated on the card or in the README" }, { "label": "Input and output", "value": "One image with a task and a query (a list of categories, a referring expression, or text to find). The model writes boxes and points as coordinate tokens from 0 to 1000, None when the target is absent, and an OBJ token after each box that the decoder turns into a binary mask or an alpha matte" }, { "label": "Recommended settings", "value": "generation_config.json sets temperature 0.7, top_p 0.8 and top_k 20. The README's results use greedy decoding with at most 1,960 visual tokens an image, and min_pixels and max_pixels are settable" } ], "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/tencent/WeVisOne-VL-2B/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/WeVisOne-VL-2B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names WeChat Vision, Tencent Inc. The domain's registration date is carried from our other Tencent listings.", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history.", "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it." ], "score": 68, "checks": [ { "check": "Legal entity named", "value": "Tencent (named in the LICENSE file's copyright line)", "points": 20, "max": 20, "state": "ok" }, { "check": "Domain age", "value": "tencent.com, registered 1998-09-14 (28 years)", "points": 15, "max": 15, "state": "ok" }, { "check": "Endpoint on the vendor's domain", "value": "no hosted endpoint", "points": 0, "max": 0, "state": "na" }, { "check": "Terms of service", "value": "published, not read by us yet", "points": 4, "max": 10, "state": "part" }, { "check": "Privacy policy", "value": "nothing hosted, not scored", "points": 0, "max": 0, "state": "na" }, { "check": "Status page", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "Changelog", "value": "published", "points": 10, "max": 10, "state": "ok" }, { "check": "security.txt", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "People named", "value": "6 named with their role, none with a public profile", "points": 7, "max": 10, "state": "part" }, { "check": "Support route", "value": "help pages, a community", "points": 5, "max": 5, "state": "ok" } ] }, "humans": { "slug": "wevisone-vl-2b", "checked": "2026-11-10", "company": "Tencent (Hy team)", "people": [ { "name": "Ma Huateng", "role": "Co-founder, chairman and CEO, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-ma-huateng.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "bio": "Ma Huateng, known as Pony Ma, co-founded Tencent in 1998 with four fellow classmates and is its chairman and chief executive. He studied computer science at Shenzhen University and wrote pager software at China Motion Telecom Development before Tencent. Its first product, the OICQ instant messenger, launched in February 1999.", "bioSources": [ "https://www.tencent.com/who-we-are/our-team/", "https://en.wikipedia.org/wiki/Ma_Huateng" ], "page": "/people/ma-huateng/" }, { "name": "Xu Chenye", "role": "Co-founder and Chief Information Officer, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-xu-chenye.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "page": "/people/xu-chenye/" }, { "name": "Zhang Zhidong", "role": "Co-founder, Advisor Emeritus and Honorary Dean of Tencent Academy, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-zhang-zhidong.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "page": "/people/zhang-zhidong/" }, { "name": "Chen Yidan", "role": "Co-founder and Advisor Emeritus, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-chen-yidan.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "bio": "Chen Yidan, also known as Charles Chen, co-founded Tencent in 1998 and was its chief administrative officer until March 2013, also leading the Tencent Charity Foundation. He remains an Advisor Emeritus at Tencent and in 2016 founded the Yidan Prize, which recognises contributions to education research and development.", "bioSources": [ "https://www.tencent.com/who-we-are/our-team/", "https://en.wikipedia.org/wiki/Chen_Yidan" ], "page": "/people/chen-yidan/" }, { "name": "Zeng Liqing", "role": "Co-founder and Advisor Emeritus, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-zeng-liqing.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "page": "/people/zeng-liqing/" }, { "name": "Yao Shunyu", "role": "Chief AI Scientist, Tencent", "bio": "Yao Shunyu is Tencent's chief AI scientist and spoke for the company when it released the Hy3 preview in April 2026. He joined Tencent from OpenAI in 2025, where he worked on language agents built on large language models. He studied in Tsinghua University's Yao Class and holds a doctorate in computer science from Princeton.", "bioSources": [ "https://www.tencent.com/tencent-unveils-hy3-preview-model-enhances-agent-capabilities-and-real-world-usability/", "https://en.wikipedia.org/wiki/Shunyu_Yao" ], "page": "/people/yao-shunyu/" } ], "peopleNote": "Tencent's five co-founders as its management team page names them (read 28 October 2026), and its chief AI scientist, whom Tencent's Hy3 preview announcement quotes on the model. The Hy3 model card credits only the Tencent Hy Team. Tencent's president, Martin Lau, and its other senior officers are on the same page but are left out to keep the list to founders and the person speaking for the model.", "companySocial": { "linkedin": "https://www.linkedin.com/company/tencentglobal", "x": "https://x.com/TencentGlobal", "github": "https://github.com/Tencent-Hunyuan" }, "support": { "urls": [ "https://huggingface.co/tencent/WeVisOne-VL-2B/discussions", "https://github.com/Tencent-Hunyuan/Hy-Memory/issues" ], "community": [ { "label": "Hugging Face discussions", "url": "https://huggingface.co/tencent/Hy3/discussions", "kind": "forum" } ] }, "sources": [ "https://huggingface.co/tencent/Hy3", "https://www.tencent.com/who-we-are/our-team/", "https://en.wikipedia.org/wiki/Ma_Huateng", "https://en.wikipedia.org/wiki/Chen_Yidan" ], "ownership": { "status": "public", "exchange": "Hong Kong Stock Exchange", "ticker": "0700", "checked": "2026-10-28", "sources": [ "https://www.tencent.com/investors/", "https://huggingface.co/tencent/Hy3" ] } }, "pageJsonUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.json" } }