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AI 모델 84759

• Claude Opus 5 (Xhigh)↗

  Anthropic

• MioTTS-2.6B↗

  Aratako

  **MioTTS-2.6B** is a lightweight, high-speed Text-to-Speech (TTS) model based on an LLM architecture. It is designed to generate high-quality speech in **English and Japanese** while maintaining low latency and minimal resource usage. This model supports zero-shot voice cloning and is built on top of the efficient neural audio codec **MioCodec-25Hz-24kHz**. We offer a range of model sizes to suit different performance and resource requirements.

• nitec-resnet-18-int8-xnnpack-executorch↗

  Arm

• bayon↗

  attentionlab

• Darwin-180B-RSI-Mixed-Quant-GGUF↗

  Baekpica

• Agnes-AI_Agnes-3.0-Qwen-GGUF↗

  bartowski

  **Model details:** - Parameter count: 180B (source checkpoint) - Input support: text, image (with mmproj file) - details - Speculative decoding: yes (MTP) - details - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (127.09GB) - usually a good mix of size and performance. Download instructions available here **About these sizes:** 51.7 % of this model's weights (`per_layer_token_embd` (160 wide), `ffn_down_exps` (640 wide), `hc_attn_up` (320 wide), `hc_ffn_up` (320 wide) and 2 more) have row widths that llama.cpp's 256-wide K and IQ block types cannot use, so each size gives them one of the 32-wide types instead (Q4_0 to Q8_0, or IQ4_NL), whatever type its name asks for, and the files above are closer together in size than usual. These sizes were not made, each within 1 % of the size of another file of the same kind: Q4_K_L (0.9 % from Q5_K_S), IQ2_XXS (0.7 % from IQ2_XS).

• all-hands_openhands-lm-7b-v0.1-GGUF↗

  bartowski

• wendyrv↗

  binant

• BioMistral-7B↗

  BioMistral

  Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released. **Advisory Notice!** Although BioMistral is intended to encapsulate medical knowledge sourced from high-quality evidence, it hasn't been tailored to effectively, safely, or suitably convey this knowledge within professional parameters for action. We advise refraining from utilizing BioMistral in medical contexts unless it undergoes thorough alignment with specific use cases and undergoes further testing, notably including randomized controlled trials in real-world medical environments. BioMistral 7B may possess inherent risks and biases that have not yet been thoroughly assessed. Additionally, the model's performance has not been evaluated in real-world clinical settings. Consequently, we recommend using BioMistral 7B strictly as a research tool and advise against deploying it in production environments for natural language generat

• FLUX.2-small-decoder↗

  Black Forest Labs

  `FLUX.2 Small Decoder` is a distilled VAE decoder that serves as a **drop-in replacement** for the standard FLUX.2 decoder. It delivers faster decoding and lower VRAM usage with minimal to zero quality loss. The encoder remains unchanged. 1. **~1.4x faster decoding** compared to the full decoder. 2. **~1.4x less VRAM** at decode time, enabling higher resolutions without running out of memory. 3. **~28M decoder parameters** (vs ~50M in the full decoder) thanks to narrower channel widths (`[96, 192, 384, 384]` vs `[128, 256, 512, 512]`). 4. **Minimal quality loss** — images are almost identical. 5. Available under the Apache 2.0 license. Compatible with all open FLUX.2 models: - FLUX.2-klein-4B - FLUX.2-klein-9B - FLUX.2-klein-9b-kv - FLUX.2-dev