Tencent Researchers Launch Tencent HY-MT1.5: A New Translation Fashions That includes 1.8B and 7B Fashions Designed for Seamless on-Machine and Cloud Deployment

Tencent Hunyuan researchers have launched HY-MT1.5, a multilingual machine translation household that targets each cellular units and cloud programs with the identical coaching recipe and metrics. HY-MT1.5 consists of two translation fashions, HY-MT1.5-1.8B and HY-MT1.5-7B, helps mutual translation throughout 33 languages with 5 ethnic and dialect variations, and is offered on GitHub and Hugging Face below open weights.

Mannequin household and deployment targets

HY-MT1.5-7B is an upgraded model of the WMT25 championship system Hunyuan-MT-7B. It’s optimized for explanatory translation and combined language eventualities, and provides native help for terminology intervention, contextual translation and formatted translation.

HY-MT1.5-1.8B is the compact variant. It has lower than one third the parameters of HY-MT1.5-7B however delivers comparable translation efficiency within the reported benchmarks. After quantization, the 1.8B mannequin can run on edge units and help actual time translation.

The quantized HY-MT1.5-1.8B operates on units with about 1 GB of reminiscence and reaches a median response time of about 0.18 seconds for Chinese language inputs of round 50 tokens, whereas surpassing mainstream industrial translation APIs in high quality. HY-MT1.5-7B targets server and excessive finish edge deployment, the place latency round 0.45 seconds is appropriate in alternate for greater high quality.

Holistic coaching framework

The analysis group defines HY-MT1.5 as a translation particular language mannequin skilled with a multi stage pipeline.

The pipeline has 5 essential elements:

1. Normal pre coaching: The bottom mannequin is first pre-trained on giant scale multilingual textual content with a language modeling goal. This builds shared representations throughout languages.
2. MT oriented pre coaching: The mannequin is then uncovered to parallel corpora and translation oriented targets. This step aligns the era distribution with actual translation duties moderately than open ended textual content era.
3. Supervised fantastic tuning: Top quality sentence and doc degree parallel information is used to fantastic tune the mannequin with supervised loss. This stage sharpens literal correctness, area protection and course particular habits, akin to ZH to EN versus EN to ZH.
4. On coverage distillation from 7B to 1.8B: HY-MT1.5-7B is used as a trainer for HY-MT1.5-1.8B. The analysis group collects about 1 million monolingual prompts throughout the 33 languages, runs them by way of the trainer and makes use of reverse Kullback Leibler divergence on the coed rollouts to match the trainer distribution. This yields a 1.8B scholar that inherits many of the 7B mannequin’s translation habits with a lot decrease value.
5. Reinforcement studying with rubrics based mostly analysis: Within the closing stage, each fashions are optimized with a bunch relative coverage optimization fashion algorithm and a rubrics based mostly reward mannequin. Human reviewers rating translations on a number of axes akin to accuracy, fluency, idiomaticity and cultural appropriateness. The reward mannequin distills these scores and guides the coverage replace.

This pipeline is particular to machine translation. It differs from chat oriented LLM coaching by combining translation centric supervised information, on coverage distillation throughout the translation area and RL tuned with fantastic grained translation rubrics.

Benchmark outcomes towards open and industrial programs

HY-MT1.5 is evaluated on Flores 200, WMT25 and a Mandarin to minority language benchmark utilizing XCOMET-XXL and CometKiwi.

Key outcomes from the above Desk within the report:

• On Flores 200, HY-MT1.5-7B reaches XCOMET-XXL scores of 0.8690 for ZH to XX, 0.9093 for EN to XX and 0.8098 for XX to XX. It outperforms translation specialised fashions akin to iFLYTEK Translator and Doubao Translator and matches or exceeds medium sized basic fashions like Qwen3-235B-A22B.
• On WMT25, HY-MT1.5-7B reaches XCOMET-XXL 0.6159. That is about 0.065 greater than Gemini 3.0 Professional and considerably above translation oriented fashions akin to Seed-X-PPO-7B and Tower-Plus-72B. HY-MT1.5-1.8B scores 0.5308, which nonetheless exceeds many medium sized basic fashions and translation programs.
• On Mandarin to minority language pairs, HY-MT1.5-7B achieves 0.6174 in XCOMET-XXL, greater than all baselines together with Gemini 3.0 Professional. The 1.8B variant reaches 0.5806 and nonetheless surpasses a number of very giant fashions like DeepSeek-V3.2.

In human analysis on a 0 to 4 scale for Chinese language to English and English to Chinese language, HY-MT1.5-1.8B achieves a median rating of two.74, which is greater than Baidu, iFLYTEK, Doubao, Microsoft and Google translator programs below the identical protocol.

Sensible options for product use

The fashions expose three immediate pushed capabilities that matter in manufacturing programs:

1. Terminology intervention: A immediate template enables you to inject time period mappings akin to “混元珠 → Chaos Pearl”. With out the mapping, the mannequin outputs an ambiguous transliteration. With the mapping, it enforces a constant area particular time period. That is vital for authorized, medical or model constrained content material.
2. Context conscious translation: A second template accepts a context block plus the sentence to translate. The report exhibits the phrase “pilot” misinterpreted as an individual when context is absent. When a paragraph about TV sequence is added, the mannequin appropriately interprets “pilot” as an episode.
3. Format preserving translation: A 3rd template wraps the supply in tags and marks spans with tags. The instruction forces the mannequin to maintain tags and output inside tags. This enables HTML or XML like textual content to outlive translation with construction preserved.

These are carried out as immediate codecs, so they’re out there even whenever you name the general public weights by way of customary LLM stacks.

Quantization and edge deployment

HY-MT1.5-1.8B is evaluated with FP8 and Int4 submit coaching quantization utilizing GPTQ.

The above Desk 4 exhibits:

• FP8 retains XCOMET-XXL scores very near the total precision mannequin, for instance 0.8379 versus 0.8361 for ZH to XX.
• Int4 reduces measurement additional however introduces clear high quality drops on Flores 200.

On Hugging Face, Tencent publishes each FP8 and GPTQ Int4 variants for HY-MT1.5-1.8B and HY-MT1.5-7B, together with GGUF variations for native inference stacks. Quantization is the mechanism that permits the reported 1 GB reminiscence deployment and low latency on client {hardware}.

Key Takeaways

• HY-MT1.5 is a 2 mannequin translation household, HY-MT1.5-1.8B and HY-MT1.5-7B, supporting mutual translation throughout 33 languages plus 5 dialect or variant kinds, launched with open weights on GitHub and Hugging Face.
• HY-MT1.5-1.8B is a distillation based mostly edge mannequin that runs on about 1 GB reminiscence with round 0.18 seconds latency for 50 token Chinese language inputs, whereas attaining trade main efficiency amongst fashions of comparable measurement and surpassing most industrial translation APIs.
• HY-MT1.5-7B is an upgraded WMT25 champion system that reaches roughly 95 p.c of Gemini 3.0 Professional on Flores 200 and surpasses it on WMT25 and Mandarin minority benchmarks, competing with a lot bigger open and closed fashions.
• Each fashions are skilled with a holistic translation particular pipeline that mixes basic and MT oriented pre coaching, supervised fantastic tuning, on coverage distillation and reinforcement studying guided by rubric based mostly human analysis, which is vital to their high quality and effectivity commerce off.
• HY-MT1.5 exposes manufacturing oriented options by way of prompts, together with terminology intervention, context conscious translation and format preserving translation, and ships FP8, Int4 and GGUF variants so groups can deploy on units or servers with customary LLM stacks.

Try the Paper, Mannequin Weights on HF and GitHub Repo. Additionally, be happy to observe us on Twitter and don’t neglect to hitch our 100k+ ML SubReddit and Subscribe to our E-newsletter. Wait! are you on telegram? now you possibly can be a part of us on telegram as nicely.

Michal Sutter is an information science skilled with a Grasp of Science in Information Science from the College of Padova. With a stable basis in statistical evaluation, machine studying, and information engineering, Michal excels at remodeling complicated datasets into actionable insights.