全站搜索
AI 模型 84760
- tau-0-wm↗
sii-research
- Midnight-Miqu-70B-v1.5↗
sophosympatheia
This is a DARE Linear merge between sophosympatheia/Midnight-Miqu-70B-v1.0 and migtissera/Tess-70B-v1.6. This version is close in feel and performance to Midnight Miqu v1.0 but I think it picked up some goodness from Tess. Their EQ Bench scores are virtually the same and their post-EXL2 quant perplexity scores were the same too. However, Midnight Miqu v1.5 passes some tests I use that Midnight Miqu v1.0 fails, without sacrificing writing quality. This model was designed for roleplaying and storytelling and I think it does well at both. It may also perform well at other tasks but I have not tested its performance in other areas. Try the following context template for use in SillyTavern. It might help, although it's a little heavy on tokens. If you save the text as a .json file, you can import it directly.
- Hypnos-i2-32B↗
squ11z1
- SRPO↗
腾讯
- stable-diffusion-xl-refiner-1.0↗
Stability AI
Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations. - **Developed by:** Stability AI - **Model type:** Diffusion-based text-to-image generative model - **License:** CreativeML Open RAIL++-M License - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L). - **Resources for more information:** Check out our GitHub Repository and the SDXL report on arXiv. For research purposes, we recommned our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popoular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. Clipdrop provides free SDXL inference.
- SRR-BERT-Leaves↗
StanfordAIMI
- StartLux-Decision-v1.1-27B-AutoThink-GGUF↗
startlux-models
Formats: BF16 · **GGUF** · MLX 8-bit · MLX 4-bit · Code: StartLuxLabs/StartLux-Decision · Paper: arXiv 2610.17309 Decision Index 0.3, public subset, as of 11 October 2026: the StartLux-Decision Instant models against other decision models of each size. Figure 1 of the technical report . The DecisionPivot column is the mean chance-corrected accuracy over the seven layers of the benchmark's scorer on its 7,800 core items, from the report's measurements: the GGUF files on llama.cpp (release b11549), the BF16 weights on vLLM. For an AutoThink model the rows therefore also compare two engines. The Q8_0 file scored 2.7 points above BF16 (95% interval +1.3 to +4.1), but as the two runs used different engines, the difference does not isolate the format. Q8_0 is the recommended file; Q4_K_M lost up to 3.8 points across the sizes.
- StartLux-Decision-v1-9B-Q4_K_M-GGUF↗
startlux-models
StartLux-Decision-v1-9B in Q4_K_M (4-bit) as a GGUF file for llama.cpp. The decision server in this repository turns it into the same typed decisions, with a probability for every option, as the original model. "Same decision" is the share of the 231 public JevBench items on which the file picks the same answer as the original weights run through the `startlux_decision` package, with the same prompts, readout and temperatures. Q8_0 and Q4_K_M are llama.cpp's standard quantizations of BF16, without an importance matrix. `-ngl 99` puts every layer on the GPU (CUDA or Metal); leave it out on a CPU-only machine. llama.cpp has to be recent enough for this model (build b10454 or newer).
- FlatLay-Male-Model-LoRA-Demo↗
strangerzonehf
- euclaise_Ferret_7B-GGUF↗
tensorblock