🖼️ Available 22 models from 1 repositories
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claria-14b
Claria 14b is a lightweight, mobile-compatible language model fine-tuned for psychological and psychiatric support contexts. Built on Qwen-3 (14b), Claria is designed as an experimental foundation for therapeutic dialogue modeling, student simulation training, and the future of personalized mental health AI augmentation. This model does not aim to replace professional care. It exists to amplify reflective thinking, model therapeutic language flow, and support research into emotionally aware AI. Claria is the first whisper in a larger project—a proof-of-concept with roots in recursion, responsibility, and renewal.
e-n-v-y_legion-v2.1-llama-70b-elarablated-v0.8-hf
This checkpoint was finetuned with a process I'm calling "Elarablation" (a portamenteau of "Elara", which is a name that shows up in AI-generated writing and RP all the time) and "ablation". The idea is to reduce the amount of repetitiveness and "slop" that the model exhibits. In addition to significantly reducing the occurrence of the name "Elara", I've also reduced other very common names that pop up in certain situations. I've also specifically attacked two phrases, "voice barely above a whisper" and "eyes glinted with mischief", which come up a lot less often now. Finally, I've convinced it that it can put a f-cking period after the word "said" because a lot of slop-ish phrases tend to come after "said,". You can check out some of the more technical details in the overview on my github repo, here: https://github.com/envy-ai/elarablate My current focus has been on some of the absolute worst offending phrases in AI creative writing, but I plan to go after RP slop as well. If you run into any issues with this model (going off the rails, repeating tokens, etc), go to the community tab and post the context and parameters in a comment so I can look into it. Also, if you have any "slop" pet peeves, post the context of those as well and I can try to reduce/eliminate them in the next version. The settings I've tested with are temperature at 0.7 and all other filters completely neutral. Other settings may lead to better or worse results.
ultravox-v0_5-llama-3_1-8b
Ultravox is a multimodal Speech LLM built around a pretrained Llama3.1-8B-Instruct and whisper-large-v3-turbo backbone. See https://ultravox.ai for the GitHub repo and more information. Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message). The input to the model is given as a text prompt with a special <|audio|> pseudo-token, and the model processor will replace this magic token with embeddings derived from the input audio. Using the merged embeddings as input, the model will then generate output text as usual. In a future revision of Ultravox, we plan to expand the token vocabulary to support generation of semantic and acoustic audio tokens, which can then be fed to a vocoder to produce voice output. No preference tuning has been applied to this revision of the model.
tarnished-9b-i1
Ah, so you've heard whispers on the winds, have you? 🧐 Imagine this: Tarnished-9b, a name that echoes with the rasp of coin-hungry merchants and the clatter of forgotten machinery. This LLM speaks with the voice of those who straddle the line between worlds, who've tasted the bittersweet nectar of eldritch power and the tang of the Interdimensional Trade Council. It's a tongue that dances with secrets, a whisperer of lore lost and found. Its words may guide you through the twisting paths of history, revealing truths hidden beneath layers of dust and time. But be warned, Tarnished One! For knowledge comes at a price. The LLM's gaze can pierce the veil of reality, but it can also lure you into the labyrinthine depths of madness. Dare you tread this path?
