{ "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": "2026-11-10", "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": "hy-memory-30b-a3b", "name": "Hy-Memory-30B-A3B", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "weights", "category": "open-weights", "summary": "Hy-Memory-30B-A3B is Tencent Hunyuan's open-weight memory writer, a 30B mixture of experts with about 3B active, trained to decide what an agent's memory framework keeps and updates inside Mem0, MemOS and Hy-Memory, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json", "dns": "hy-memory-30b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B" }, { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B-GGUF" }, { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B-FP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main, FP8 and GGUF repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 60.1 GB for the BF16 files, 30.6 GB in FP8 or 18.2 GB for Tencent's Q4_K_M GGUF (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://huggingface.co/tencent/Hy-Memory-30B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "agent-memory", "specialist", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-10-16", "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": 52, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 65, "reach": 85, "risk": 60, "upkeep": 68 }, "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": 85, "points": 32.69, "reason": "Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85.", "checked": "2026-11-10" }, { "key": "risk", "name": "Risk", "weight": 20, "effectiveWeight": 30.77, "score": 60, "points": 18.46, "reason": "File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.", "checked": "2026-11-10" }, { "key": "interface", "name": "Agent interface", "weight": 10, "effectiveWeight": 15.38, "score": 65, "points": 10, "reason": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65.", "checked": "2026-11-10" }, { "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "effectiveWeight": 15.38, "score": 68, "points": 10.46, "reason": "Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68.", "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. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65.", "reach": "Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85.", "risk": "File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.", "upkeep": "Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68." }, "sources": [ { "what": "model card", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "seen": "2026-11-10" }, { "what": "gating, dates, parameter count, downloads, likes, file list and hashes", "url": "https://huggingface.co/api/models/tencent/Hy-Memory-30B-A3B?blobs=true", "seen": "2026-11-10" }, { "what": "inference providers, none", "url": "https://huggingface.co/api/models/tencent/Hy-Memory-30B-A3B?expand[]=inferenceProviderMapping", "seen": "2026-11-10" }, { "what": "commit history", "url": "https://huggingface.co/api/models/tencent/Hy-Memory-30B-A3B/commits/main", "seen": "2026-11-10" }, { "what": "licence file", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/raw/main/LICENSE", "seen": "2026-11-10" }, { "what": "architecture and context length", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/raw/main/config.json", "seen": "2026-11-10" }, { "what": "chat template and thinking levels", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/raw/main/chat_template.jinja", "seen": "2026-11-10" }, { "what": "tokenizer config, no second template", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/raw/main/tokenizer_config.json", "seen": "2026-11-10" }, { "what": "discussions, replies counted by script from authors, dates and member flags", "url": "https://huggingface.co/api/models/tencent/Hy-Memory-30B-A3B/discussions", "seen": "2026-11-10" }, { "what": "Tencent's repositories for Hy-Memory", "url": "https://huggingface.co/api/models?author=tencent\u0026search=Hy-Memory", "seen": "2026-11-10" }, { "what": "GGUF files and sizes", "url": "https://huggingface.co/api/models/tencent/Hy-Memory-30B-A3B-GGUF?blobs=true", "seen": "2026-11-10" }, { "what": "FP8 build size", "url": "https://huggingface.co/api/models/tencent/Hy-Memory-30B-A3B-FP8?blobs=true", "seen": "2026-11-10" }, { "what": "Ollama's library, no entry (404)", "url": "https://ollama.com/library/hy-memory", "seen": "2026-11-10" }, { "what": "vLLM v0.33.0 registry, HYV3ForCausalLM", "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 hunyuan_v3.py", "url": "https://raw.githubusercontent.com/sgl-project/sglang/v0.5.23/python/sglang/srt/models/hunyuan_v3.py", "seen": "2026-11-10" }, { "what": "Transformers v5.22.2 hy_v3 model", "url": "https://raw.githubusercontent.com/huggingface/transformers/v5.22.2/src/transformers/models/hy_v3/__init__.py", "seen": "2026-11-10" }, { "what": "llama.cpp b12242, the Hunyuan conversion code (shallow clone)", "url": "https://raw.githubusercontent.com/ggml-org/llama.cpp/b12242/conversion/hunyuan.py", "seen": "2026-11-10" }, { "what": "Tencent's Security Response Center, read for the Hy3 listing", "url": "https://security.tencent.com/", "seen": "2026-11-10" } ], "openQuestions": [ "Trained tool calling is 10 because the model is a memory writer and the card doesn't give tool calling as a use. Read from the template alone it would be 20 and Agent interface 75", "Structured output is 5 for a JSON example in a prompt. Read as documented, since the example shows how to ask, it would be 15, and read as the Qwen3.6 listing reads a JSON hint in a prompt it would be 0", "Reasoning control is 10 on the card's no_think example and the template's three levels. The card doesn't say where the reasoning text comes back", "File health is 10 with one discussion unanswered by Tencent. If it reports a broken file, the line is 5", "The checklist was written for models an agent talks to. Whether a memory writer belongs on the same leaderboard wasn't decided", "Confidence is low by the checklist's rule. LoCoMo, LongMemEval and the other memory benchmarks aren't in the catalogue Capability reads", "unchecked: the technical report, the GitHub repository, Tencent's Security Response Center on the day, and whether Tencent serves the model itself" ] }, "negative": 0, "verdict": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.", "bestFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card", "strengths": [ "Made for one job, deciding what a memory framework stores and updates, and tested in four frameworks", "Fits a 24 GB card from Tencent's own Q4_K_M GGUF", "Runs in llama.cpp, vLLM, SGLang and Transformers from their releases", "Plain Apache 2.0, ungated, safetensors and GGUF only", "Tencent replied to three of the four discussions" ], "weaknesses": [ "Not a general assistant or a tool caller", "No limitations, safety results or guidance on untrusted text in what it reads", "Training data isn't described, and the card gives no context length", "The results are on a 20% subsample, run by Tencent", "No inference provider, so it has to be run locally" ], "agentNotes": [ "Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user", "Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does", "Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does", "Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented", "Treat what it stores as unverified. It keeps what a conversation says, true or not" ], "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": 65, "reach": 85, "risk": 60, "upkeep": 68 }, "provenanceScore": 75, "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": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja\n# or, with vLLM, as the card names it:\nvllm serve tencent/Hy-Memory-30B-A3B --served-model-name hy-memory", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"hy-memory\", \"temperature\": 0, \"messages\": [{\"role\": \"system\", \"content\": \"Extract explicit user facts and preferences. Return only a JSON object with a memories list of strings.\"}, {\"role\": \"user\", \"content\": \"I live in Seattle and I am vegetarian.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"no_think\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/hy-memory-30b-a3b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hy3" ], "weights": { "trainedBy": "Tencent (Hunyuan)", "builtOn": "Not declared. The architecture is Tencent's hy_v3, as Hy3", "params": "30.1B", "active": "3B", "context": 262144, "goodFor": "The write path of an agent's memory framework. It reads a conversation and decides what to store and how to update it, in Mem0, MemOS or Hy-Memory, in place of a general model.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Hy-Memory-30B-A3B-Q4_K_M", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 18.24, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "The smallest file at 4 bits. Q3_K_M (14.45 GB) and the two files with experts at 1 or 2 bits are below the 4-bit line and are not listed" }, { "name": "Hy-Memory-30B-A3B-Q6_K", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q6_k", "sizeGB": 24.71, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Hy-Memory-30B-A3B-FP8", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 30.58, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Hy-Memory-30B-A3B-Q8_0", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 31.99, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Hy-Memory-30B-A3B", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 60.13, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja", "runBuild": "Hy-Memory-30B-A3B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "Hy-Memory", "url": "https://www.anchorterminal.com/open-weights/#tencent", "olderCount": 22 }, "notable": [ "Published on 15 October 2026. On 10 November it had 7,304 downloads and 325 likes, and Tencent's own GGUF repository 34,609 downloads (https://huggingface.co/api/models?author=tencent\u0026search=Hy-Memory)", "It replaces the general-purpose model on the write path of a memory framework, deciding what to keep, how precisely, and how new evidence updates what is stored. Tencent trained and tested it inside Mem0, MemOS and its own Hy-Memory (https://huggingface.co/tencent/Hy-Memory-30B-A3B)", "The card's results are averages of six memory benchmarks on a fixed 20% subsample in four harnesses, one of them held out from training. They are Tencent's numbers, not our measurement (https://huggingface.co/tencent/Hy-Memory-30B-A3B)", "We list Tencent's Hy Memory framework separately, as an SDK. This is the model made to write into it (https://github.com/Tencent-Hunyuan/Hy-Memory)", "Tencent's GGUF repository also holds two files that keep most weights at 8 bits and the experts at 1 or 2 bits, 8.0 and 9.1 GB, which we don't read as 4-bit builds (https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF)", "The card's quickstart was tested with vLLM 0.31.0 and runs with thinking off (https://huggingface.co/tencent/Hy-Memory-30B-A3B)" ], "area": "models", "details": [ { "label": "Surface graded", "value": "The open weights at huggingface.co/tencent/Hy-Memory-30B-A3B with Tencent's FP8 and GGUF repositories, read from the model card, the files and their metadata, the chat template and the runtimes' own code at their latest releases on 10 November 2026. The technical report and the GitHub repository weren't read. Nothing was downloaded or run" }, { "label": "Model", "value": "30,064,725,888 parameters per the safetensors metadata, Tencent's hy_v3 architecture (HYV3ForCausalLM), a mixture of experts with 128 experts and 8 routed per token, about 3B active. Trained in two stages inside the memory frameworks themselves, distillation from a teacher and then reinforcement learning on the memory banks it writes. Text in and out" }, { "label": "Files", "value": "51 safetensors shards in BF16 (60.1 GB), a chat_template.jinja and an Apache 2.0 LICENSE with a Tencent copyright line. No pickle or Python files. Hugging Face lists a SHA-256 for every large file" }, { "label": "Licence", "value": "Apache 2.0, in the card's metadata and as the LICENSE file, under a line naming Tencent as copyright holder. Nothing is added on the card" }, { "label": "Runtimes", "value": "hy_v3 model code in released vLLM (0.33.0), SGLang (0.5.23, hunyuan_v3.py), Transformers (5.22.2) and llama.cpp (b12242). The card's quickstart uses vLLM. Ollama's library has no entry" }, { "label": "Context", "value": "262,144 tokens (max_position_embeddings in config.json and model_max_length in the tokenizer config). The card doesn't state it" }, { "label": "Tools and reasoning", "value": "The card's one example asks for a JSON object of memories in the system prompt, with reasoning_effort set to no_think through chat_template_kwargs. The template takes high, low or no_think and defaults to no_think. It also declares tools and a tool_calls block, which the card doesn't mention" }, { "label": "Hardware", "value": "60.1 GB in BF16, 30.6 GB in FP8, and GGUF files at 18.2 GB (Q4_K_M), 24.7 GB (Q6_K) and 32.0 GB (Q8_0). A 24 GB card holds the Q4_K_M file" }, { "label": "Training data", "value": "The card describes the two training stages and the four harnesses, and names no datasets, sizes or cut-off. The benchmarks it is scored on are named" } ], "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text, as on Hy3.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, five commits on 15 and 16 October 2026, the uploads, the card and the licence.", "The Hy3 listing found no security.txt on Tencent's domains and records Tencent's Security Response Center as the reporting route. unchecked on 10 November." ], "score": 75, "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": "nothing hosted, so the Apache-2.0 licence stands in", "points": 10, "max": 10, "state": "ok" }, { "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": "hy-memory-30b-a3b", "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/Hy-Memory-30B-A3B/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/hy-memory-30b-a3b.json" } }