- OmniVoice int8 - Chatterbox Multilingual fp16 - VoxCPM2 bf16 - Fish Audio S2 Pro fp16
Languages:
- English - German - Modern Standard Arabic - Spanish - Mandarin Chinese
The benchmark uses Google FLEURS test clips as dataset references. Each row includes the reference audio, generated audio, speaker similarity, WER/CER, generated audio length, and RTF.
Main result in this run: OmniVoice was the strongest all-around row set, with 0.707 mean speaker cosine across all five languages, 0.0% ASR error, and mean RTF 0.45. VoxCPM2 bf16 was especially strong on Arabic speaker match. Fish Audio S2 Pro showed strong German/Arabic similarity but slower RTF. Chatterbox Multilingual was competitive on Arabic and Spanish.
This is an engineering benchmark, not a human MOS study. The speaker-similarity values should be compared within this table because every row uses the same local speaker-embedding pipeline.
I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached “Space of the Week”! A few Spaces are still topping the list even after many months.
Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.
We’re excited to announce that Unsloth has joined the PyTorch Ecosystem! 🔥🦥
Unsloth is an open-source project that makes training & running models more accurate and faster with less compute. Our mission is to make local AI accessible to everyone. Thanks to all of you for making this possible! 💕
Python-Code-Large is a large-scale corpus of Python source code comprising more than 2 million rows of Python code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the Python ecosystem.
By providing a high-volume, language-specific corpus, Python-Code-Large enables systematic experimentation in Python-focused model training, domain adaptation, and downstream code understanding tasks.
Python-Code-Large addresses the need for a dedicated Python-only dataset at substantial scale, enabling focused research across data science, backend systems, automation, scientific computing, and AI-driven Python environments.
In the Text-to-Video arena, Seedance 2.0 has first secured a spot in the LMArena Top 10, while Kling 3.0 has topped the Artificial Analysis leaderboard, with the Kling family claiming 7 spots in the top 15.
🚀 TRL v0.29.0 introduces trl-training: an agent-native training skill.
This makes the TRL CLI a structured, agent-readable capability, allowing AI agents to reliably execute training workflows such as: - Supervised Fine-Tuning (SFT) - Direct Preference Optimization (DPO) - Group Relative Policy Optimization (GRPO)
We’re excited to see what the community builds on top of this.
If you’re working on AI agents, alignment research, or scalable RL training infrastructure: give TRL v0.29.0 a try! 🤗
if you like it give the demo a little star and send a shoutout to : @MaxLSB@jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
You can now fine-tune embedding models in our free Unsloth notebook! 🤗
Fine-tuning embedding models improves retrieval & RAG by aligning vectors to your domain-specific notion of similarity, improving search, clustering, and recommendations on your data.
Got to 1199.8 tokens/sec with Devstral Small -2 on my desktop GPU workstation. vLLM nightly. Works out of the box with Mistral Vibe. Next is time to test the big one.
• Together, we applied advanced topology optimization to redesign critical brackets of the manipulator, achieving a 57–76% reduction in structural deflection.
• Our updated model also demonstrated a major stress decrease — from 93 MPa down to 25 MPa — all while staying within the allowed weight increase.
• Although we didn’t fully reach the target tip deviation of 0.3 mm (best achieved: 0.41 mm), the project gave us valuable insights and a solid foundation for the next design iteration.
soniqo @aufklarer 
