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. Credit belongs to the miner, and to Prism ML for Bonsai 2.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
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-XII-27B-PQ2_0.gguf`); `Ingot-XII-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.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
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-XIII-27B-PQ2_0.gguf`); `Ingot-XIII-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.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
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。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
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-XV-27B-PQ2_0.gguf`); `Ingot-XV-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.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
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 rest of its 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-XVI-27B-PQ2_0.gguf`); `Ingot-XVI-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.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
**Teutonic-II 110B Genesis** is an open-weight, compute-efficient sparse Mixture-of-Experts (MoE) language model with approximately **110B total parameters** and an estimated **~7.3B activated parameters per token**. The **Genesis checkpoint is initialized with random weights and has not yet undergone pretraining**, providing a clean starting point for continued pretraining and decentralized model improvement through **Teutonic** on **Bittensor Subnet 3**. The model is built on a modified **MiMoV2-style architecture**, combining sparse MoE layers with hybrid Global and Sliding Window Attention, Grouped Query Attention, asymmetric QK/V head dimensions, partial Rotary Position Embeddings, and attention-sink mechanisms. **In addition to adopting MiMoV2 architectural concepts, the Teutonic team introduces a modification to the MoE implementation that enables expert sharing.** Teutonic-II 110B Genesis is designed as a compute-efficient large-scale sparse language model architecture for continued pretraining and decentralized model improvement.
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기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
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기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
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기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
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기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
Depth Anything V2 is trained from 595K synthetic labeled images and 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features: - more fine-grained details than Depth Anything V1 - more robust than Depth Anything V1 and SD-based models (e.g., Marigold, Geowizard) - more efficient (10x faster) and more lightweight than SD-based models - impressive fine-tuned performance with our pre-trained models Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release, but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. The original Depth Anything model was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al., and was first released in this repository. Depth Anything V2 leverages the DPT architecture with a DINOv2 backbone.
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입력 텍스트→ 출력 텍스트
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입력 텍스트→ 출력 텍스트
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입력 텍스트→ 출력 텍스트
On-device titles and descriptions: a short factual title and a one- to two-sentence description for any passage of text. Get a title and a one or two sentence description for any passage of text, on device. Title is fine-tuned on transcript clips, but it works on any prose. The register is deliberately plain, with no emoji, no hashtags and no clickbait, and a description identifies *this* passage rather than its topic.
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입력 텍스트→ 출력 텍스트
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입력 텍스트→ 출력 텍스트
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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입력 텍스트→ 출력 텍스트
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입력 텍스트→ 출력 텍스트
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입력 텍스트→ 출력 텍스트
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입력 텍스트→ 출력 텍스트
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기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
