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AI 模型 2119
- Ingot-IV-27B-Q2↗
dendriteholdings
A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it. It is Prism ML's Ternary Bonsai 2 27B, refined: the same ternary codes, with the per-block scales and the norm weights re-tuned by the miner to bring it closer to Qwen3.8-27B. The weights are the miner's upload, byte for byte (`Ingot-IV-27B-PQ2_0.gguf`); `Ingot-IV-27B-PTQ1_0.gguf` repacks them losslessly at 1.75 bits per weight. Credit belongs to the miner, and to Prism ML for Bonsai 2.
- EUPE-ViT-B↗
facebook
Running AI models on smart edge devices can unlock various user experiences, but presents challenges due to limited compute and the need to handle multiple tasks simultaneously. This requires a vision encoder with small size but powerful and versatile representations. We present our method, Efficient Universal Perception Encoder (EUPE), which offers both inference efficiency and universally good representations for diverse downstream tasks. We achieve this by distilling from multiple domain-expert foundation vision encoders. Unlike previous agglomerative methods that directly scale down from multiple teachers to an efficient encoder, we demonstrate the importance of first scaling up to a large proxy teacher and then distilling from this single teacher. Experiments show that EUPE achieves on-par or better performance than individual domain experts of the same size on diverse task domains and also outperforms previous agglomerative encoders. These are Vision Transformer and ConvNeXt models trained following the method described in the EUPE paper. 6 models are provided: - 3 ViT models including ViT-B16, ViT-S16, ViT-T16 - 3 ConvNeXt models including ConvNeXt-{T/S/B}
- polyglot-lion-1.7b↗
knoveleng
**Polyglot-Lion-1.7B: High-accuracy multilingual ASR for Singapore — English, Mandarin, Tamil & Malay** Polyglot-Lion-1.7B was developed by Quy-Anh Dang and Chris Ngo at Knovel Engineering and presented in the report "Polyglot-Lion: Efficient Multilingual ASR for Singapore via Balanced Fine-Tuning of Qwen3-ASR". The model is obtained by fine-tuning Qwen3-ASR-1.7B exclusively on publicly available speech corpora covering Singapore's four official languages. It utilizes a balanced sampling strategy that equalizes the number of training utterances per language and deliberately omits language-tag conditioning, allowing the model to learn to identify languages implicitly from audio.
- hyenadna-large-1m-seqlen-hf↗
LongSafari
Welcome! HyenaDNA is a long-range genomic foundation model pretrained on context lengths of up to **1 million tokens** at **single nucleotide resolution**. You'll see models of different sizes and sequence lengths. There are also original weights-only versions of each model in the LongSafari organization, which are designed to be loaded with the original github repo. These models have identical outputs to the models in the collection above, just different interfaces. In this brief code sample we demonstrate fine-tuning HyenaDNA on a sequence classification task. This sample uses the `medium` checkpoint, with a maximum sequence length of 160k nucleotides. Note that training will fail if you use a sequence length longer than the maximum supported length for your chosen checkpoint.
- e5-base↗
intfloat
**News (May 2023): please switch to e5-base-v2, which has better performance and same method of usage.** Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf.
- Qwopus3.8-27B-Flash-V2-GGUF↗
Jackrong
> [!NOTE] > **Qwopus3.8-27B-Flash-V2** is a renewed post-training release of Qwopus3.8-27B-Flash, whose foundation is Qwen3.8-27B. > [!TIP] > **Revised Rewards and Reinforcement Learning · Improved Python Formatting** Qwopus3.8-27B-Flash-V2 is a renewed post-training release of Qwopus3.8-27B-Flash, which itself is built on Qwen3.8-27B. The goal remains the same: preserve enough capability for demanding work while reducing ineffective reasoning and making long-running agent workloads more practical.
- Signal-3.8-27B↗
agentionai
> [!IMPORTANT] > **Update 2026-09-13 — use these settings for stable, short reasoning** > Temperature 0.6, min-p 0.05 (top-p 0.95, top-k 20), KV cache q8_0 or f16, and the MTP draft head on. > Without min-p, long answers can run away into repetition on some draws; a 4-bit value cache makes that > deterministic. Both are fixed by the settings above, on Signal and on the base model alike. > [!WARNING] > The weights in this repository were updated on 2026-09-13 (the GGUF tiers were rebuilt from them). The first release's tiers could, rarely, > repeat an answer line when sampling without the draft head; the rebuilt tiers showed no loop or cap > in 55 traces under the same conditions. If you downloaded before that date, re-download. AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-27B designed for lower generation latency and better token efficiency. On our held-out general-prompt evaluation, Signal produces **57% fewer answer tokens** and uses **52% fewer thinking tokens**, while matching or improving the measured answer quality of the base model.
- DeepSeek-R1-Distill-Llama-8B↗
AlSamCur123
- vit-tiny-patch16-224↗
WinKawaks
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the timm repository. This model is used in the same way as ViT-base.
- sam2_hiera_base_plus.fb_r896_2pt1↗
timm