Hy-MT2-30B-A3B
Hy-MT2-30B-A3B is a 30-billion parameter mixture-of-experts (MoE) multilingual translation model from Tencent, released May 2026. It supports 33 languages and instruction-following translation tasks (terminology, style, personalization, structured data). Apache 2.0 licensed, ungated, with 107k downloads. The model card claims outperformance vs. DeepSeek-V4-Pro and commercial APIs, but provides no independent benchmarks or security audit details.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | tencent |
| Parameters | 30.1B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | translation |
| Gated on HuggingFace | No |
| Downloads | 107.9k |
| Likes | 469 |
| Last updated | 2026-05-26 |
| Source | tencent/Hy-MT2-30B-A3B |
What Hy-MT2-30B-A3B is
30B-A3B is a MoE-based causal LM trained for translation across 33 languages (zh, en, fr, pt, es, ja, tr, ru, ar, ko, th, it, de, vi, ms, id, tl, hi, pl, cs, nl, km, my, fa, gu). Supports transformers library (>=5.6.0). Distributed as safetensors. Context length unknown. Model card recommends temperature 0.7, top_p 1.0, top_k -1, repetition_penalty 1.0, max_tokens 4096. No quantized variants listed for 30B-A3B itself (1.8B has FP8, GGUF, 1.25-bit options). Inference code examples provided for transformers.
Run Hy-MT2-30B-A3B locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="tencent/Hy-MT2-30B-A3B")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE based on 30B MoE architecture: Single A100 80GB or multi-GPU setup (e.g., 2× A100 40GB) for full inference. Quantized (FP8/INT8) variants not listed for 30B; 16-bit inference likely requires 80GB+ VRAM. Verify exact MoE active parameter count and sparse activation pattern before deployment.
Card does not mention LoRA, QLoRA, or other fine-tuning compatibility. Given size (30B) and MoE architecture, LoRA is plausible but requires custom integration. Smaller variants (7B, 1.8B) more feasible for parameter-efficient tuning. Test on representative domain data before production.
When to avoid it — and what to weigh
- Context-heavy or long-document translation — Context length unknown; card does not state max input length. Long documents may require chunking and context loss.
- Real-time, ultra-low-latency requirements — 30B model size implies multi-GPU inference; no latency benchmarks provided. Smaller variants (1.8B) may be required for sub-second SLAs.
- Specialized domain translation without fine-tuning — Card claims domain performance but does not detail which domains or provide domain-specific benchmarks. Custom fine-tuning may be necessary.
- Regulatory/compliance audit trail required — No security audit, data provenance, or bias evaluation published. Suitable for internal/non-regulated use only without additional assessment.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Permits commercial use, modification, and distribution with attribution and liability disclaimer.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use. However: (1) no indemnification clause; (2) card does not detail training data provenance or licensing (e.g., whether training corpus is commercially usable); (3) no warranty. Recommend legal review of training data sources and downstream liability before production deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
No security audit, adversarial robustness testing, or bias evaluation disclosed. Translation models may be susceptible to prompt injection or adversarial inputs; treat outputs as machine-generated, not authoritative. Training data provenance unknown—no statement on data filtering, GDPR compliance, or PII handling. Recommend sandboxing untrusted model outputs in production.
Alternatives to consider
DeepSeek-V4-Pro (mentioned competitor)
Card claims Hy-MT2-7B and 30B-A3B outperform DeepSeek in fast-thinking mode. Independent eval required; DeepSeek is a general-purpose model, not translation-specific.
Google Translate API or Azure Translator
Established, compliant, and documented alternatives for production multilingual translation. Commercial SLA and support; suitable if latency/cost trade-off favors managed services.
NLLB-200 (Meta, open-source)
Smaller, mature, 200-language multilingual model. No MoE; lower inference cost. Fewer instruction-following features but well-documented and battle-tested.
Ship Hy-MT2-30B-A3B with senior software developers
Evaluate Hy-MT2-30B-A3B for your content and code translation pipeline. Review the model card, architecture report, and hardware requirements. Start with the smaller 1.8B or 7B variant for feasibility testing, then scale to 30B-A3B if latency permits.
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Hy-MT2-30B-A3B FAQ
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Work with a software development agency
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Ready to integrate multilingual translation?
Evaluate Hy-MT2-30B-A3B for your content and code translation pipeline. Review the model card, architecture report, and hardware requirements. Start with the smaller 1.8B or 7B variant for feasibility testing, then scale to 30B-A3B if latency permits.