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AI モデル 84759
- Llama-3-8B-instruct↗
AI-Sweden-Models
- Z-Image-Fun-Lora-Distill↗
alibaba-pai
The 2602 model tends to produce blurry images with sigmas below 0.500, as the distillation model was not trained on certain steps. The 2603 model introduces a random timesteps strategy, making it better adapted to sigmas below 0.500. As shown below, when using kl_optimal, many sigmas fall below 0.500. The 2603 model handles these cases correctly, while the 2602 model does not. Note that although kl_optimal is used in the figure, we still recommend using the simple scheduler for inference. Z-Image-Fun-Lora-Distill-8-Steps-2602 Z-Image-Fun-Lora-Distill-4-Steps-2602 Z-Image-Fun-Lora-Distill-8-Steps
- FLUX.1-dev-INT8-ConvRot↗
AX1Y2JP
- Llama3-Crab-DPO-GGUF↗
bartowski
- MS-Schisandra-22B-v0.3-GGUF↗
bartowski
- Prismatic-12b-GGUF↗
bartowski
- Tower-Babel_Babel-83B-Chat-GGUF↗
bartowski
- zerofata_MS3.2-PaintedFantasy-v4-24B-GGUF↗
bartowski
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (zerofata_MS3.2-PaintedFantasy-v4-24B-Q8_0) or download them all in place (./)
- vllm-translategemma-12b-it↗
chbae624
This is a modified version of google/translategemma-12b-it optimized for deployment with vLLM. **No retraining was performed.** Only configuration files and the chat template were modified. Model weights are identical to the original. As of 2025-01-29, vLLM does not natively support TranslateGemma's custom structured input format. See vllm-project/vllm#32446 for the upstream tracking issue. Until that is merged, this repo provides a workaround by modifying configuration files to make TranslateGemma compatible with vLLM's standard chat API.
- hayate↗
choogee