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AI 模型 84760

• sapiens2-pose-5b↗

  facebook

  308-keypoint top-down pose estimation including detailed face (274 keypoints), hand, and foot keypoints. Predictions follow the Sociopticon keypoint format. This repository contains the **5B Pose Estimation** checkpoint, finetuned from the Sapiens2-5B pretrained backbone.

• ultravox-v0_6-llama-3_1-8b↗

  fixie-ai

  Ultravox is a multimodal Speech LLM built around a pretrained LLM (Llama, Gemma, Qwen, etc) and a speech encoder (whisper-large-v3-turbo) backbone. Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message). The input to the model is given as a text prompt with a special ` ` pseudo-token, and the model processor will replace this magic token with embeddings derived from the input audio. Using the merged embeddings as input, the model will then generate output text as usual. In v0.6 series, ultravox models are trained on expanded Hindi speech data, resulting in significantly improved speech understanding performance on Hindi and modest degradation on other languages. Additionally, the v0.6 models are also trained on noise datasets for improved noise robustness and the ability to output a special string ``((noise))`` if the input audio is too noisy or doesn't contain clear speech.

• Blake-XTM-Arc-3B-V1-GGUF↗

  Flexan

• hin-deva-10mb-ppt-Dp-100mb-packed-bfdiso_seed455↗

  francesca9805

• Laya-GGUF↗

  ggml-org

  This is a decision model, to be used via `/v1/systemone` API. See https://github.com/ggml-org/llama.cpp/pull/29818 > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert

• Qwen3-ASR-0.6B-GGUF↗

  ggml-org

  This model is converted from Qwen/Qwen3-ASR-0.6B to GGUF using `convert_hf_to_gguf.py`

• Gemini 3.8 Flash (Medium)↗

  Google

• gemma-4-31B-it-qat-q4_0-gguf↗

  Google

  Hugging Face | GitHub | Launch Blog | Documentation | Technical Report > [!Note] > This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. > Four versions of the QAT checkpoints are available: > * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. > * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B. > * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B. > * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B, and 31B. > * **Assistant Compatibility**: When using multi-token prediction (speculative decoding) with an assistant model alongside a QAT target model, the assistant model must also be a QAT checkpoint with the same precision to ensure compatibility. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.

• multiberts-seed_4-step_1800k↗

  Google

• parakeet-tdt-0.6b-v3-gguf↗

  handy-computer

  GGUF conversions of nvidia/parakeet-tdt-0.6b-v3 for use with transcribe.cpp. Ported from upstream commit 6d590f7, pinned 2026-04-16. Validated against the NeMo reference at transcribe.cpp commit bf0d0b7 on 2026-04-18. Offline multilingual speech-to-text covering 25 European languages. A Conformer encoder with a TDT/RNNT transducer decoder. Takes a 16 kHz mono WAV and produces a transcript with optional token-level timestamps. Not a streaming model and does not translate.