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AnimeGen-I2V

aidealab

**AnimeGen I2V** is a image-to-video generation model specialized for Japanese anime-style video generation. AnimeGen I2V was developed by **AIdeaLab, Inc.** as part of its effort to build high-quality generative AI models for animation and video production. The model was developed with support from **GENIAC — Generative AI Accelerator Challenge**, a Japanese government-backed project led by the Ministry of Economy, Trade and Industry of Japan (METI) and the New Energy and Industrial Technology Development Organization (NEDO), whose goal is to strengthen domestic capabilities in generative AI development. AnimeGen I2V is based on **Wan 2.2 I2V A14B** and further trained to improve anime-style motion, character rendering, color composition, and visual consistency for animation-oriented use cases.

Video generation
Input Image→ Output Video
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RadJEPA

AIDElab-IITBombay

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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**Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct**. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements: **Qwen3-Coder-30B-A3B-Instruct** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: **262,144 natively**. **NOTE: This model supports only non-thinking mode and does not generate `` `` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**

Text generation
Input Text→ Output Text
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Visual QA
Input Text · Image→ Output Text
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RudolfV-2

Aignostics

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RudolfV-2-B

Aignostics

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RudolfV-2-S

Aignostics

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This model was converted to GGUF format from `evolveon/Qwen3-14B-abliterated` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

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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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Text generation
Input Text→ Output Text
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