> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. - **Agentic Coding:** the model now handles frontend workflows and repository-level reasoning with greater fluency and precision. - **Thinking Preservation:** we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.
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View details ↗This is a FinBERT model fine-tuned with the FinnSentiment dataset. 90% of sentences were used for training and 10% for evaluation. FinBERT-FinnSentiment is licensed under the CC BY 4.0 License (same as FinBERT and the FinnSentiment dataset).
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View details ↗Phonon-2 is the most accurate open speech recognition model for English under 900 MB. Across the Open ASR Leaderboard's seven English sets it averages 5.21 % word error, beating models multiple times its size in raw bytes. Set for set it holds the accuracy of the 2.5 GB full-precision parakeet-tdt-0.6b-v3 it derives from, reaching 100.8 % of that model's word accuracy on parliamentary speech and beating it on meetings, from a download 15 times smaller. It transcribes an hour of audio in about 20 seconds on an M5 MacBook Air (174x realtime), at 143x on eight Zen 5 cores (16 vCPU) and at 6,680x on one H100 in batches of 128. † Open ASR Leaderboard's published row; the other rows use its code on the full test sets. * Size from the parameter count at 16 bits.
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