humanizer

Model · Text generation

RPBizkit-v10-12B

Ricardo_estep

"Experimental Mistral Nemo Mix" using... "Model Stock merge with Mergekit". This is a mix of some known "RP Uncensored" models. I'm not responsible of the use of this. From the "errors" without head, to "the simpler excellence". This model is "quality". "Version V6" leaked stability, "Version V9" leaked creativity. This model has "pruned weights" full of "pure stable creativity", from original "base" models "tunned for dark RP". No more "mixes, from mixed mixes", without the "fixmistralregex" patch problem. All done with patience in mind... Have fun! TheDrummer/UnslopNemo-12B-v4.1 as base. The Tokenizer & Embedding Sizes (131072) are clean + It's "supposed to" support now a full "128K…

Model · Text generation

RPBizkit-v7-12B

Ricardo_estep

"Experimental Mistral Nemo Mix" using... "Model Stock merges with Mergekit". This is a mix of some known "RP Uncensored" models. I'm not responsible of the use of this. After noticing some "errors" on my previous versions including the "RemiX" one. I decided to go "simplier". "Model Stock" cleanly mixes and filters out the noise from each model. natong19/Mistral-Nemo-Instruct-2407-abliterated # Base! natong19/Mistral-Nemo-Instruct-2407-abliterated # Base! natong19/Mistral-Nemo-Instruct-2407-abliterated # Base! natong19/Mistral-Nemo-Instruct-2407-abliterated # Base! natong19/Mistral-Nemo-Instruct-2407-abliterated # Base!.\mergekitconfig1 # Intelligence.\mergekitconfig2 # RP &…

Vigyan-3B 4× MoE is an experimental Sparse Mixture of Experts model upcycled from 4 specialized domain LoRA adapters (Science, Technology, Engineering, Mathematics). It was assembled to evaluate whether post-hoc expert stitching on compact language models ($\le 3\text{B}$) can deliver multi-domain specialization at single-expert inference latency. - shreyansh12183/vigyan-3b-adapter-science - shreyansh12183/vigyan-3b-adapter-technology - shreyansh12183/vigyan-3b-adapter-engineering - shreyansh12183/vigyan-3b-adapter-mathematics 1. Delimiter Blindness: Without general conversational anchor replay during adapter training, the model degrades into token echo loops when receiving general chat…

Merged checkpoint produced by the family-aware Delta-P2S experiment package.

Model · Text generation

vllm-translategemma-12b-it

Bae chang hyun

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. This conversion is based entirely on the work done by Infomaniak-AI/vllm-translategemma-4b-it. The same conversion approach was applied to the 12B model.…

Model · Text generation

Qwen1.5-MoE-A2.7B

Qwen

Qwen1.5-MoE is a transformer-based MoE decoder-only language model pretrained on a large amount of data. For more details, please refer to our blog post and GitHub repo. Qwen1.5-MoE employs Mixture of Experts (MoE) architecture, where the models are upcycled from dense language models. For instance, Qwen1.5-MoE-A2.7B is upcycled from Qwen-1.8B. It has 14.3B parameters in total and 2.7B activated parameters during runtime, while achieving comparable performance to Qwen1.5-7B, it only requires 25% of the training resources. We also observed that the inference speed is 1.74 times that of Qwen1.5-7B. The code of Qwen1.5-MoE has been in the latest Hugging face transformers and we advise you to…