Tencent-Hunyuan/Hy-MT2

This page introduces an external open-source repository. It is not an HDATF product.

What it does

Hy-MT2 is a family of translation language models released by the Tencent Hunyuan team in three sizes, 1.8B, 7B and 30B-A3B, the last being a mixture-of-experts (MoE) model. The models translate among 33 languages, and Korean and Japanese are in the supported language table. Users give the source text and translation instructions as a prompt and receive the translation, and the README provides prompt templates for terminology, style, personalization, delimiters and structured data. The maximum context length is 8192 tokens and there is no default system prompt, so long documents need to be split before translation. FP8 and GGUF quantized versions, which shrink the model files, go down to 2-bit and 1.25-bit formats, and the README says the 1.25-bit version of the 1.8B model needs about 440 MB of storage. Running it requires transformers 5.6.0 or later with trust_remote_code, a setting that executes code from the model repository, or vLLM and SGLang built from source, and the README says low-bit formats in llama.cpp need the STQ kernel from pull request 22836. LoRA or full fine-tuning runs through LLaMA-Factory with DeepSpeed, and the training guide asks for one GPU with 24 GB or more for 1.8B, one 80 GB GPU for 7B LoRA and eight 80 GB GPUs for 30B. The IFMTBench benchmark for instruction-following translation is published under CC-BY-4.0 and scores six constraint types with rules and an LLM judge. The model license is Apache 2.0 according to LICENSE.txt, and the README benchmark comparisons were not independently checked here.

License

Custom license Custom or non-standard license. Read the license file before any reuse.

More in this category

• Ebony-Vinyl/dsh-our-free-model
  This dsh plugin connects an agent to externally hosted models rather than running model weights locally. It sends conversation messages, tool results and optional images to upstream gateways and returns model answers or tool calls. Its adapter refreshes model catalogs, probes availability and translates several response protocols into the host's streaming format. It also offers a local OpenAI-compatible forwarding service and local usage records. Starting requires a compatible dsh host, a supported Node.js runtime and network access. Kilo officially allows anonymous requests to free models, but applies IP-based rate limits. The OpenCode route additionally imitates client fingerprints, so its authorization and service terms need separate review rather than treating every route as an officially supported public API. Other account channels and the desktop EAC route have separate login or authorization requirements, and the free gateways must not be treated as unlimited, private or suitable for sensitive production data.
• deepseek-ai/DeepGEMM
  A CUDA tensor core kernel library for language-model computations, including matrix multiplication and fused MoE. Compiles kernels at runtime through DeepJIT.
• Edge0-AI/Edge0
  Edge0 is a streaming MoE inference framework using SSD expert offloading, Recover-LoRA and routing prediction. Its README distinguishes released platform engines from a unified framework planned for Q4 2026.
• Vibra-Ingenn/Janus
  A single Go binary that runs gguf models on a local machine, using llama.cpp through Vulkan on AMD, Intel or NVIDIA graphics with a CPU fallback, and exposes an OpenAI compatible API for chat completions and models. It swaps models without a restart, splits reasoning output into its own field, and reads the chat template from gguf metadata, with no Python and no Docker required.
• magnitudedev/magnitude
  An inference engine for agents that compiles and tunes kernels on the user's hardware. It runs open models on Apple Silicon, NVIDIA, AMD or CPU and connects to existing agents through a desktop app and CLI.

Only repositories in the ranked Trendshift lists are included, and the lists are used only to find candidates. We do not copy their ranks. Descriptions, licenses and star counts come from each GitHub repository. The notes are our own reading. We have not tested these projects, and a place on a trending list does not prove quality.