SDAR-1.7B-Chat
JetLM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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SDAR-30B-A3B-Chat
JetLM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 ↗SDAR-4B-Chat
JetLM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 ↗SDAR-4B-Chat-b32
JetLM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 ↗SDAR-8B-Chat
JetLM**SDAR** (**S**ynergy of **D**iffusion and **A**uto**R**egression) model is a new large language model that integrates autoregressive (AR) and discrete diffusion modeling strategies. It combines the efficient training paradigm of AR models with the highly parallel inference capability of diffusion models, while delivering performance fully on par with SOTA open-source AR models. At the same time, SDAR sets a new benchmark as the most powerful diffusion language model to date. We highlight three major conclusions from our study: > [!IMPORTANT] > Take-home message > > - **Balanced Efficiency:** SDAR unifies the **efficient training** of AR models with the **parallel inference** of diffusion, achieving both fast training and inference. > - **Fair Comparisons:** In rigorously controlled experiments, SDAR achieves **on-par general task performance** with strong AR baselines, ensuring credibility and reproducibility. > - **Superior Learning Efficiency:** On complex scientific reasoning tasks (e.g., GPQA, ChemBench, Physics), SDAR shows **clear gains over AR models** of the same scale, approaching or even exceeding leading closed-source systems. For **SDAR** models, inference hyperparameters are set to: `block_length = 4`, `denoising_steps = 4`, greedy decoding.
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View details ↗SDAR-8B-Chat-b32
JetLM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 ↗SDAR-VL-Instruct-4B
JetLM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 ↗jetmoe-8b
jetmoe1. JetMoE-8B is **trained with less than $ 0.1 million** 1 **cost but outperforms LLaMA2-7B from Meta AI**, who has multi-billion-dollar training resources. LLM training can be **much cheaper than people previously thought**. 2. JetMoE-8B is **fully open-sourced and academia-friendly** because: - It **only uses public datasets** for training, and the code is open-sourced. No proprietary resource is needed. - It **can be finetuned with very limited compute budget** (e.g., consumer-grade GPU) that most labs can afford. 3. JetMoE-8B **only has 2.2B active parameters** during inference, which drastically lowers the computational cost. Compared to a model with similar inference computation, like Gemma-2B, JetMoE-8B achieves constantly better performance.
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View details ↗jetspec-qwen3-8b
JetSpec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 ↗sn38-v11-1
jetx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 ↗sn38-v11-3
jetx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 ↗sn38-v11-4
jetx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 ↗sn38-v11-5
jetx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 ↗The image conditioned version of TRELLIS, a large 3D genetive model. It was introduced in the paper Structured 3D Latents for Scalable and Versatile 3D Generation.
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View details ↗EuroLLM-1.7B-Instruct-mlx-4Bit
jevoma4362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 ↗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 ↗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 ↗This repository contains four experimental, single-file NVFP4 Hybrid checkpoints for MiniMax H3. They combine the FL2VA quality base with the Ref2VA reference-conditioning pathway using the tensor-selection strategy published by `smhfacct/Minimax-H3-fl2va-ref2va-hybrid-models`. All four checkpoints (`b15-49`, `b20-49`, `b25-49`, and `b30-49`) have completed manual ComfyUI validation with actual video generation. All four checkpoints also passed complete structural and per-tensor source verification. Generation examples and their settings are listed below. Browse the generation examples: two Ref2VA cases and two FL2VA cases, with input images, full prompts, shared settings, and output videos for each diffusion model. Within each case, only the diffusion model changes.
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View details ↗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 ↗gemma-4-31b-it-heretic-ara-gguf
jfiekdjdk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 ↗Qwen3-8B-abliterated-Q5_K_M-GGUF
jfiekdjdk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 ↗ada-coder-qwen2.5-1.5b
jgalego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 ↗amalia-desenrasca-9b
jgalego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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