Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Qwen3-MT

Qwen3-MT is a machine translation model developed by Alibaba Cloud's Qwen team, released on July 25, 2025. It is fine-tuned from Qwen3 with a lightweight Mixture-of-Experts backbone and trained on trillions of multilingual tokens spanning formal, technical, and conversational text. The model covers 92 major languages and prominent dialects, reaching over 95% of the global population.

General
Release date25 Jul 2025
DeveloperQwen / Alibaba Cloud
TypeMachine translation model (MoE fine-tune)
LicenseCommercial API
DocumentationAlibaba Cloud Model Studio
APIDashScope via Qwen API Platform

Core Features

  • 92 languages: covers major world languages and prominent dialects, reaching 95% of the global population.
  • Terminology control: allows custom terminology dictionaries to keep brand names, technical terms, and product names consistent.
  • Domain prompting: a domain hint lets the model adapt output style for legal, medical, technical, or conversational text.
  • Translation memory: integrates past translation pairs so repeated segments stay consistent across large documents.
  • Competitive pricing: priced at $0.5 per million tokens, significantly lower than dense large models for translation workloads.

Performance

Qwen3-MT outperforms comparably-sized models on translation benchmarks, including GPT-4.1-mini and Gemini-2.5-Flash, while remaining competitive with larger models like GPT-4.1 and Gemini-2.5-Pro on translation quality metrics.

Tools and Resources

Ecosystem and Integrations

  • Served through Alibaba Cloud DashScope, accessible with the OpenAI-compatible endpoint or the native DashScope SDK.
  • Supports batch translation for high-volume document workflows.
  • Term dictionaries and translation memory integrate via API request parameters, requiring no custom fine-tuning.

Get started by generating an API key on the Qwen API Platform and following the Model Studio translation guide.

Edit on GitHub

Qwen Qwen3-MT AI technology Hackathon projects

Discover innovative solutions crafted with Qwen Qwen3-MT AI technology, developed by our community members during our engaging hackathons.

VoiceHire: AI Recruitment Workspace

VoiceHire: AI Recruitment Workspace

Every recruiter knows the pain: dozens of CVs to screen, interview slots to coordinate, notes scattered across tools, and hiring decisions that rely more on memory than evidence. VoiceHire brings it all into a single workspace where AI does the repetitive work so recruiters can focus on what matters. Upload a batch of resumes and the platform extracts structured profiles with skills, experience, and education. Describe a role and it auto-generates a detailed job posting. With one click, the Candidate Matcher ranks every applicant against the requirements scoring on skills, experience, past performance, and culture fit and explains exactly why each candidate is a strong match or where their gaps are. The real breakthrough is the interview itself. When a candidate joins, six AI agents collaborate across three dedicated rooms. One builds a tailored competency rubric from the job and resume. Another generates probing questions targeting specific skill gaps. A voice agent delivers them naturally while transcribing every word. Two evidence extractors one technical, one behavioral analyze responses for genuine signals. A skeptic runs in the background challenging weak claims and flagging inconsistencies. After the interview, three agents deliberate as a committee, weighing evidence from both sides before reaching a hiring recommendation. Recruiters see everything live: competency coverage updating in real time, integrity flags appearing as suspicious behavior is detected, and multiple interviews running side by side. When it's done, there's a complete evidence portfolio with every question, every answer, every signal extracted, and the full deliberation transcript. No more "I think they were good" just auditable, defensible hiring.

ROCKET — AI Performance Researcher for AMD

ROCKET — AI Performance Researcher for AMD

ROCKET is a multi-agent system (Profiler → Planner → Implementer → Validator) that takes any PyTorch model, profiles it on AMD MI300X, hypothesizes which optimizations will help, applies them from a bounded toolbox of five transformations (dtype_cast, torch_compile, sdpa_attention, input_padding, kv_cache_config), validates output correctness, and measures the speedup — completely on its own. On a real AMD Instinct MI300X (ROCm 7) running Qwen2.5-7B-Instruct at batch=8, prompt 256 + new 512, ROCKET achieved a measured 2.93× throughput speedup: 62.59 → 183.47 tokens per second. The agent tried 5 tools and kept 1 (bf16 cast); correctly reverted the four that didn't beat the validator's 2% keep threshold — exactly the judgment a senior performance engineer would apply. How this is different from adjacent submissions: - ROCmPort AI translates CUDA → ROCm code. ROCKET makes the model FAST. - ReplayLab records GPU experiments. ROCKET acts on them. - Sakana's AI Scientist optimizes model accuracy. ROCKET optimizes throughput on AMD silicon; the question every AMD developer asks first that no one had automated.

ROCm-Sentinel: Performance & Security Copilot

ROCm-Sentinel: Performance & Security Copilot

ROCm-Sentinel is a comprehensive solution designed to eliminate friction in the transition to the AMD ecosystem. Unlike conventional code translators, our project utilizes an agentic graph (LangGraph) to perform a deep analysis of the original software infrastructure. The system identifies digital signatures and language patterns—such as CUDA-specific dependencies—to evaluate migration viability before any code is modified. The core of the project features a specialized "Performance & Security" agent that does not just patch incompatibilities; it suggests proactive improvements to leverage AMD hardware fully. Furthermore, we include an automated audit loop: the system executes the code in Docker containers within a ROCm environment, analyzes error logs, and iteratively corrects the code to ensure total fidelity with the original. With ROCm-Sentinel, migration evolves from a technical risk into a strategic optimization.

AI Resume Intelligence System

AI Resume Intelligence System

AI Resume Intelligence System is a comprehensive end-to-end resume processing and job matching platform powered by advanced AI techniques. The system leverages vector embeddings, semantic search, and intelligent matching algorithms to analyze resumes and find the most relevant job opportunities. Key features include automated resume parsing using APILayer API with local fallback, intelligent job categorization using AMD-backed Fireworks LLMs, ATS scoring for candidate evaluation, and smart job recommendations based on semantic similarity. The system integrates multiple AI services including Fireworks API (minimax-m2p7, deepseek-v3p2 models), ChromaDB for vector storage, and real-time job search via SerpAPI. Built with Flask web framework, the application provides a user-friendly interface for uploading resumes and receiving personalized job matches. The system demonstrates production-ready deployment on Railway platform with robust error handling, platform-specific compatibility, and cost-efficient AI processing using AMD GPU infrastructure through Fireworks API. This project showcases practical AI application in HR tech, combining vector embeddings with semantic search to solve real-world recruitment challenges while maintaining scalability and cost-effectiveness.