Model Gallery

20 models from 1 repositories

Filter by type:

Filter by tags:

minimax-h3-fl2va-unpruned-q4-ggml
MiniMax-H3 served by stable-diffusion.cpp. It generates video AND stereo audio jointly from a text prompt, so a clip comes back as an MP4 with a soundtrack. Muxing the finished frames needs ffmpeg on the host. Runtime: stable-diffusion.cpp (backend stablediffusion-ggml). Partition: fl2va. DiT layout: unpruned. Format: Q4_K_M. This entry is the FL2VA partition: text-to-video, first-frame (I2VA) and first/last-frame (FL2VA) conditioning. It refuses reference conditioning (reference image or audio): that needs minimax-h3-ref2va-pruned-q4-ggml, which is a separate checkpoint. Render status: verified render (the stable-diffusion.cpp page shows a text-to-video and an image-to-video render with this file). Weights are the Q4_K_M quantisation of the DiT (~18.8 GB), the Qwen3-VL-32B MiniMax-H3 text encoder (~18 GB), the fp16 video VAE (~5 GB) and the fp32 audio VAE (~0.6 GB). MiniMax-H3 runs at 24 fps and the width and height are rounded up to a multiple of 32. The frame count is rounded up to the 17k + 5 grid.

Repository: localaiLicense: other

minimax-h3-fl2va-pruned-q4-ggml
MiniMax-H3 served by stable-diffusion.cpp. It generates video AND stereo audio jointly from a text prompt, so a clip comes back as an MP4 with a soundtrack. Muxing the finished frames needs ffmpeg on the host. Runtime: stable-diffusion.cpp (backend stablediffusion-ggml). Partition: fl2va. DiT layout: pruned. Format: Q4_K_M. This entry is the FL2VA partition: text-to-video, first-frame (I2VA) and first/last-frame (FL2VA) conditioning. It refuses reference conditioning (reference image or audio): that needs minimax-h3-ref2va-pruned-q4-ggml, which is a separate checkpoint. It is the smaller, pruned counterpart of minimax-h3-fl2va-unpruned-q4-ggml. Render status: rendered with stable-diffusion.cpp on CUDA (text-to-video, 512x288, 22 frames, with audio). Not yet rendered through LocalAI. Weights are the Q4_K_M quantisation of the DiT (~11.4 GB), the Qwen3-VL-32B MiniMax-H3 text encoder (~18 GB), the fp16 video VAE (~5 GB) and the fp32 audio VAE (~0.6 GB). MiniMax-H3 runs at 24 fps and the width and height are rounded up to a multiple of 32. The frame count is rounded up to the 17k + 5 grid.

Repository: localaiLicense: other

minimax-h3-ref2va-pruned-q4-ggml
MiniMax-H3 served by stable-diffusion.cpp. It generates video AND stereo audio jointly from a text prompt, so a clip comes back as an MP4 with a soundtrack. Muxing the finished frames needs ffmpeg on the host. Runtime: stable-diffusion.cpp (backend stablediffusion-ggml). Partition: ref2va. DiT layout: pruned. Format: Q4_K_M. This entry is the REF2VA partition, the one that takes REFERENCE conditioning: reference images, reference videos (a directory of frames, with an optional paired soundtrack) and reference audio steer the subject and the voice of the clip. It does not serve first/last-frame keyframes, and keyframes cannot be mixed with references. For plain text-to-video or keyframes use minimax-h3-fl2va-unpruned-q4-ggml. Render status: verified render (the stable-diffusion.cpp page shows a reference-to-video render with this file). Weights are the Q4_K_M quantisation of the DiT (~11.4 GB), the Qwen3-VL-32B MiniMax-H3 text encoder (~18 GB), the fp16 video VAE (~5 GB) and the fp32 audio VAE (~0.6 GB). MiniMax-H3 runs at 24 fps and the width and height are rounded up to a multiple of 32. The frame count is rounded up to the 17k + 5 grid.

Repository: localaiLicense: other

minimax-m2.1-i1
The model **MiniMax-M2.1** (base model: *MiniMaxAI/MiniMax-M2.1*) is a large language model quantized for efficient deployment. It is optimized for speed and memory usage, with quantized versions available in various formats (e.g., GGUF) for different performance trade-offs. The quantization is done by the user, and the model is licensed under the *modified-mit* license. Key features: - **Quantized versions**: Includes low-precision (IQ1, IQ2, Q2_K, etc.) and high-precision (Q4_K_M, Q6_K) options. - **Usage**: Requires GGUF files; see [TheBloke's documentation](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for details on integration. - **License**: Modified MIT (see [license link](https://github.com/MiniMax-AI/MiniMax-M2.1/blob/main/LICENSE)). For gallery use, emphasize its quantized variants, performance trade-offs, and licensing.

Repository: localaiLicense: modified-mit

minimax-m2.5-apex-i-quality

Repository: localai

minimax-m2.5-apex-i-balanced

Repository: localai

minimax-m2.5-apex-i-compact

Repository: localai

minimax-m2.5-apex-i-mini

Repository: localai

minimax-m2.5-q8-0

Repository: localai

minimax-m2.7-apex-i-quality

Repository: localai

minimax-m2.7-apex-i-balanced

Repository: localai

minimax-m2.7-apex-i-compact

Repository: localai

minimax-m2.7-apex-i-mini

Repository: localai

minimax-m2.7-apex-i-nano

Repository: localai

minimax-m2.7-ud-q4-k-m

Repository: localai

minimax-m2.7-ud-q5-k-m

Repository: localai

minimax-m2.7-ud-q6-k

Repository: localai

minimax-m2.7-q8-0

Repository: localai

minimax-m2.5
MiniMax-M2.5. Quality ladder and quantization rungs published by mudler/MiniMax-M2.5-APEX-GGUF and its unsloth counterpart; LocalAI picks the build that fits the hardware.

Repository: localai

minimax-m2.7
MiniMax-M2.7. Quality ladder and quantization rungs published by mudler/MiniMax-M2.7-APEX-GGUF and its unsloth counterpart; LocalAI picks the build that fits the hardware.

Repository: localai