# Hy-Memory-30B-A3B (slim)

> 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.

- Full: https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md (~9,500 tokens) · this version ~1,580 tokens · JSON https://www.anchorterminal.com/tools/hy-memory-30b-a3b.json · canonical https://www.anchorterminal.com/tools/hy-memory-30b-a3b
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10 · pricing https://www.anchorterminal.com/tools/hy-memory-30b-a3b#pricing · reviews https://www.anchorterminal.com/tools/hy-memory-30b-a3b#reviews
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

**C · 61/100 · rank #62 of 135 open-weight models · #52 in Open-weight models · not agent-ready · confidence low**


Assessment: 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.

## Facts

- Kind: Open-weight model · vendor: Tencent (Hy team) · category: Open-weight models · legal entity: Tencent (named in the LICENSE file's copyright line) · provenance 75/100
- Packages: huggingface `tencent/Hy-Memory-30B-A3B`, huggingface `tencent/Hy-Memory-30B-A3B-GGUF`, huggingface `tencent/Hy-Memory-30B-A3B-FP8`
- Auth: None · pricing: Free · x402: no · licence: Apache-2.0
- Benchmark probes: not run yet, so Performance is pending
- Surface graded: 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
- Model: 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
- Files: 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
- Licence: 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
- Runtimes: 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
- Context: 262,144 tokens (max_position_embeddings in config.json and model_max_length in the tokenizer config). The card doesn't state it
- Tools and reasoning: 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
- Hardware: 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
- Training data: 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

- Scores: Capability pending, Reach 85, Risk 60, Agent interface 65, Documentation and upkeep 68 · total over the 7 assessed categories
- Why:  · 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. · Risk, File safety 25, 51 safetensors shards and no pickle or Python files. · Agent interface, Chat template 20, chat_template.jinja in the repository. · Documentation and upkeep, Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chine…
- Sources: 18, open questions: 7, both in the full twin
- Capabilities: model.open-weights
- JSON: https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json
- Its vendor verifies the listing with `verify_listing` or `POST https://www.anchorterminal.com/api/v1/verify` (the full twin says how); no effect on the grade.
- Support: https://huggingface.co/tencent/Hy-Memory-30B-A3B/discussions; https://github.com/Tencent-Hunyuan/Hy-Memory/issues; more in the full twin

## Before you call it

1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user
2. 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
3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does
4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented
5. Treat what it stores as unverified. It keeps what a conversation says, true or not

## Connect

```bash
llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja
# or, with vLLM, as the card names it:
vllm serve tencent/Hy-Memory-30B-A3B --served-model-name hy-memory
```

```bash
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -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"}}'
```

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## Panel reviews (0, desk reviews from public material, no calls made)