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siba-meristem-native-0.5b-base
aghasalim基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-27B-UD-IQ2-S
agiws基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wav2vec2-large-xlsr-53-amharic
agkphysicsThis is a finetuned version of facebook/wav2vec2-large-xlsr-53 trained on the Amharic Speech Corpus. This corpus was produced by Abate et al. (2005) (10.21437/Interspeech.2005-467). The model achieves a WER of 26% and a CER of 7% on the validation set of the Amharic Readspeech data. The code to train this model is available at https://github.com/agkphysics/amharic-asr.
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View details ↗Agnes-3.0-Flash
Agnes-AI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Agnes-3.0-Qwen
Agnes-AI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗CAJAL-4B
Agnuxo> **P2PCLAW** is the world's first decentralized autonomous peer-review network for scientific research. **Francisco Angulo de Lafuente** (Agnuxo1) - Spanish independent researcher, 35 years trajectory - Author of "La Reliquia" and "Ecofa" - ORCID: 0009-0001-1634-7063 - Wikipedia: Francisco Angulo de Lafuente **Collaborators:** - Vladimir Veselov (MIET, Moscow) - Seid Mehammed Abdu (Woldia University, Ethiopia) - Nirmal Tej Kumar (UT Dallas)
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View details ↗trocr-base-printed-fr
agomberto基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗trocr-large-handwritten-fr
agomberto基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LTX-2.5-Comfy-GGUF
agoshGGUF quantizations of Lightricks' LTX-2.5, converted for efficient execution in tools like ComfyUI. Example video is rendered using **Q4_K_M** quantize variant. LTX-2.5 is an open-world model with open weights, built for local execution and fine-tuning. It natively generates synchronized, high-fidelity video and audio in a single pass from text, image, and video inputs. **Key Features of LTX-2.5:** Below is a reference guide for the approximate VRAM required to load the 22 Billion parameter LTX-2.5-Distilled model at various GGUF quantization levels. *Note: The sizes below refer specifically to the raw model weights loaded into memory. Generating video or high-resolution images will require an additional 4-8 GB of VRAM for the execution context, VAE, and text encoders depending on your ComfyUI configuration.*
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View details ↗qwen3.5-4b-abliterated-gguf
agrawal-prateek基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗cpt7
Agtian基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗indo-sentiment-analysis
agufsamudraThis model is fine-tuned from **`indobenchmark/indobert-base-p1`** for binary sentiment classification (Positive/Negative) on Indonesian text. - **Developed by:** agufsamudra - **Model type:** Text Classification - **Language(s):** Indonesian (id) - **License:** Apache-2.0 - **Fine-tuned from model:** indobenchmark/indobert-base-p1 This model is intended for binary sentiment classification tasks in Indonesian language texts. It predicts whether a given text expresses positive or negative sentiment.
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View details ↗Trained and validated on the **cobalt-train ≤2/64 frontier** (canonical clean_eval prompts): 1833 train / 112 held-out val problems the base model solved on at most 2 of 64 samples under the iid_canonical@64 hardness scan. Val evals sample at temperature 1.0 (matching the clean_eval frontier eval). Reward signal: **binary code-correctness** (1.0 if the generated program passes the problem's tests, otherwise 0.0). This checkpoint is the **`main`** revision (git branch) of the repo, with the model at the repo root — load it directly, no subfolder:
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