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AI 모델 2119

• sam2-hiera-base-plus↗

  facebook

  Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the SAM 2 paper for more information. The official code is publicly release in this repo. Refer to the demo notebooks for details.

• MEETING_SUMMARY↗

  knkarthick

  Model obtained by Fine Tuning 'facebook/bart-large-xsum' using AMI Meeting Corpus, SAMSUM Dataset, DIALOGSUM Dataset, XSUM Dataset!

• VoxCPM1.5↗

  openbmb

  **Key Improvements:** - 🔊 **Higher Quality**: 44.1kHz sampling rate preserves more high-frequency details for better voice cloning - ⚡ **More Efficient**: Reduced token rate (6.25Hz) lowers computational cost while maintaining performance - 🎓 **Fine-tuning Support**: Train personalized voice models with SFT or LoRA **Note**: Output quality depends on the prompt speech quality. VoxCPM-0.5B remains fully supported with backward compatibility. VoxCPM is a novel tokenizer-free Text-to-Speech (TTS) system that redefines realism in speech synthesis. By modeling speech in a continuous space, it overcomes the limitations of discrete tokenization and enables two flagship capabilities: context-aware speech generation and true-to-life zero-shot voice cloning.

• wav2vec2-large-xlsr-53-chinese-zh-cn↗

  jonatasgrosman

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on Chinese using the train and validation splits of Common Voice 6.1, CSS10 and ST-CMDS. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

• Krea2-Turbo-Distill-2step-LoRA↗

  lvladikov

  > 🆕 **New: a 3-step LoRA** takes Krea 2 Turbo from 8 steps down to 3, one step more than this LoRA, with more fine detail than both this and the 4-step LoRA (fine texture 1.13× the 8-step teacher's on its 22 test prompts). This 2-step LoRA stays the fastest of the three. **A quarter of the steps · 4× faster denoising (text encoding and VAE decode excluded) · fine detail at 0.92–1.02× the teacher's across all 12 trained resolutions, the grid bands at its level · colour close to the teacher's · 1 point missing of 352 on a blind prompt-adherence rubric · teacher preferred on 12 of 66 judged renders · 51,195 training samples on recorded teacher trajectories · 32 days on one RTX 3090 · released from settled weights; the project's final checkpoint.** A LoRA for **Krea 2 Turbo** that takes the model from **8 steps down to 2** — Turbo's own weights and sigmas, guidance 0.0, a quarter of the denoising passes — aiming at the best quality two steps can give. It is for **fast previews and drafts**; the **4-step LoRA** remains the recommendation for quality renders.

• MiniCPM5-2B-Base↗

  openbmb

  We are releasing **MiniCPM5-2B**, the second model in the **MiniCPM5** series, following MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA. 🏆 **2B-class open-source SOTA**: compared with strong open-source models of similar size, MiniCPM5-2B achieves SOTA performance within this comparison set. It remains competitive with 4B-class models overall, while showing its advantages over models of comparable size in coding, mathematics, long-context understanding, tool use, and agentic tasks. #capability-comparison-radar { --foreground: #171717; display: block; width: 100%; max-width: 620px; margin: 0 auto; background: #fff; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", sans-serif; } #capability-comparison-radar svg { display: block; width: 100%; height: auto; background: #fff; } #capability-comparison-radar .title { fill: var(--foreground); font-size: 16px; font-weight: 600; letter-spacing: 0; } #capability-comparison-radar .axis-label { fill: #333; font-size: 12px; font-weight: 600; } #capability-comparison-radar .ring { fill: none; stroke: rgba(128,128,128,.18); stroke-width: .8; } #capability-comparison-radar .spoke { stroke: rgba(128,128,128,.25); stroke-width: .8; } #capability-comparison-radar .ring-label { fill: #8a8a8a; font-size: 9px; } #capability-comparison-radar .series { stroke-linejoin: round; } #capability-comparison-radar .series.primary { stroke-width: 2.4; } #capability-comparison-radar .legend-label { fill: #171717; font-size: 12px; font-weight: 600; } #capability-comparison-radar .legend-average { fill: #666; font-size: 11px; } #capability-comparison-radar .legend-swatch { rx: 3; } @media (max-width: 900px) { #capability-comparison-radar { overflow-x: auto; } #capability-comparison-radar svg { min-width: 620px; } }

• Tiel-Coder-35B-A3B-GGUF↗

  peculiar-ragdoll

  *Tiel* is the fast coder of the arsenal. At 4-bit quantization and 22 GB it fixes real codebase issues at the rate (and speed, with the right GPU) of Opus 4.6 medium, while holding the best multi-turn conversation of any local model we have measured. It is also cheerfully bad at trivia. > **This is Ornith-1.5-35B-A3B re-quantized > dynamically with our own imatrix and carrying the Sharp chat template** > inside the GGUF. Find TielCoder MTP GGUFs here. > **The uncensored and improved CyberTiel is out now!** Get it here. **Where it stands.** On 25 SWE-bench-Live problems Tiel fixes 12 — the same as Opus 4.6 (medium), four more than Ornith-1.5 itself, three more than Nail, and four more than Sonnet 5 (medium). Among models of its own class it is first; the ones ahead are dense 27Bs and Opus 5. Its time per attempt is also steadier than Nail's: an 8.6 minute median against 7.2, but a 12.3 minute mean against 15.7, because it lacks Nail's tail of expensive attempts.

• Ternary-Bonsai-2-4B-gguf↗

  prism-ml

  Prism ML Website  |  Demo & Examples  |  Discord A 4B-class vision-language model with ternary transformer weights, for llama.cpp (CUDA, Metal, CPU). - **Ternary language weights** across embeddings, attention projections, MLP projections and the (tied) LM head, stored with a group-wise FP16 scale. - **Three GGUF packings** of the same weights, consumed directly by custom kernels and never expanded back to FP16: **PTQ1_0** (dense trits, 0.95 GB), **PQ2_0** (2-bit slots, 1.15 GB) and **Q2_0** (1.21 GB). - **Vision input** through a separate mmproj pack (Q8_0, or BF16 for reference precision), loaded only when an image arrives. - **262K-token context**, inherited from the Qwen3.5-4B hybrid-attention backbone (24 linear-attention and 8 full-attention layers).

• twitter-roberta-base-2022-154m↗

  cardiffnlp

  This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 (from original checkpoint, no incremental updates). A large model trained on the same data is available here. These 154M tweets result from filtering 220M tweets obtained exclusively from the Twitter Academic API, covering every month between 2018-01 and 2022-12. Filtering and preprocessing details are available in the TimeLMs paper. Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.

• wav2vec2-large-robust↗

  facebook

  The large model pretrained on 16kHz sampled speech audio. Speech datasets from multiple domains were used to pretrain the model: - Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data - CommonVoice: crowd-source collected audio data; read-out text snippets - Switchboard: telephone speech corpus; noisy telephone data - Fisher: conversational telephone speech; noisy telephone data **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. Authors: Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli