This model was originally intended to be a DADA finetune of Llama-3.1-8B-Instruct but the results were unsatisfactory. So it received some additional finetuning on a rawtext dataset and now it is utterly cursed.
Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.
Llama Guard 3 was aligned to safeguard against the MLCommons standardized hazards taxonomy and designed to support Llama 3.1 capabilities. Specifically, it provides content moderation in 8 languages, and was optimized to support safety and security for search and code interpreter tool calls.
llama3.1-8B-Chinese-Chat is an instruction-tuned language model for Chinese & English users with various abilities such as roleplaying & tool-using built upon the Meta-Llama-3.1-8B-Instruct model. Developers: [Shenzhi Wang](https://shenzhi-wang.netlify.app)*, [Yaowei Zheng](https://github.com/hiyouga)*, Guoyin Wang (in.ai), Shiji Song, Gao Huang. (*: Equal Contribution) - License: [Llama-3.1 License](https://huggingface.co/meta-llama/Meta-Llla...
m-3.1-8B/blob/main/LICENSE) - Base Model: Meta-Llama-3.1-8B-Instruct - Model Size: 8.03B - Context length: 128K(reported by [Meta-Llama-3.1-8B-Instruct model](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct), untested for our Chinese model)
"Llama3.1-70B-Chinese-Chat" is a 70-billion parameter large language model pre-trained on a large corpus of Chinese text data. It is designed for chat and dialog applications, and can generate human-like responses to various prompts and inputs. The model is based on the Llama3.1 architecture and has been fine-tuned for Chinese language understanding and generation. It can be used for a wide range of natural language processing tasks, including language translation, text summarization, question answering, and more.
Lumimaid 0.1 -> 0.2 is a HUGE step up dataset wise.
As some people have told us our models are sloppy, Ikari decided to say fuck it and literally nuke all chats out with most slop.
Our dataset stayed the same since day one, we added data over time, cleaned them, and repeat. After not releasing model for a while because we were never satisfied, we think it's time to come back!
The LLM model is a large language model trained on a combination of datasets including nothingiisreal/c2-logs-cleaned, kalomaze/Opus_Instruct_25k, and nothingiisreal/Reddit-Dirty-And-WritingPrompts. The training was performed on a combination of English-language data using the Hugging Face Transformers library.
Trained on LLaMA 3.1 8B Instruct at 8K context using a new mix of Reddit Writing Prompts, Kalo's Opus 25K Instruct and c2 logs cleaned This version has the highest coherency and is very strong on OOC: instruct following.
This is the second in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of Qwen1.5 32B.
The Tifa role-playing language model is a high-performance language model based on a self-developed 220B model distillation, with a new base model of qwen2-7B. The model has been converted to gguf format for running in the Ollama framework, providing excellent dialogue and text generation capabilities.
The original model was trained on a large-scale industrial dataset and then fine-tuned with 400GB of novel data and 20GB of multi-round dialogue directive data to achieve good role-playing effects.
The Tifa model is suitable for multi-round dialogue processing, role-playing and scenario simulation, EFX industrial knowledge integration, and high-quality literary creation.
Note: The Tifa model is in Chinese and English, with 7.6% of the data in Chinese role-playing and 4.2% in English role-playing. The model has been trained with a mix of EFX industrial field parameters and question-answer dialogues generated from 220B model outputs since 2023. The recommended quantization method is f16, as it retains more detail and accuracy in the model's performance.
Crazy idea that what if you put the LoRA from crestf411/sunfall-peft on top of princeton-nlp/gemma-2-9b-it-SimPO and therefore this exists solely for that purpose alone in the universe.
Crazy idea that what if you put the LoRA from crestf411/sunfall-peft on top of princeton-nlp/gemma-2-9b-it-SimPO and therefore this exists solely for that purpose alone in the universe.
The LLM model is the "Seeker-9b" model, which is a large language model trained on a diverse range of text data. It has 9 billion parameters and is based on the "lodrick-the-lafted" repository. The model is capable of generating text and can be used for a variety of natural language processing tasks such as language translation, text summarization, and text generation. It supports the English language and is available under the Apache-2.0 license.
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.
Meta-Llama-3-8B Instruct (now at 12.2B) with Brainstorm process that increases its performance at the core level for any creative use case. It has calibrations that allow it to exceed the logic solving abilities of the original model. The Brainstorm process expands the reasoning center of the LLM, reassembles and calibrates it, introducing subtle changes into the reasoning process. This enhances the model's detail, concept, connection to the "world", general concept connections, prose quality, and prose length without affecting instruction following. It improves coherence, description, simile, metaphors, emotional engagement, and takes fewer liberties with instructions while following them more closely. The model's performance is further enhanced by other technologies like "Ultra" (precision), "Neo Imatrix" (custom imatrix datasets), and "X-quants" (custom application of the imatrix process). It has been tested on multiple LLaMA2, LLaMA3, and Mistral models of various parameter sizes.
Meta-Llama-3-8B Instruct (now at 8.9B) is an enhanced version of the LLM model, specifically designed for creative use cases such as story writing, roleplaying, and fiction. This model has been augmented through the "Brainstorm" process, which involves expanding and calibrating the reasoning center of the LLM to improve its performance in various creative tasks. The enhancements brought by this process include more detailed and nuanced descriptions, stronger prose, and a greater sense of immersion in the story. The model is capable of generating long and vivid content, with fewer clichés and more focused, coherent narratives. Users can provide more instructions and details to elicit stronger and more engaging responses from the model. The "Brainstorm" process has been tested on multiple LLM models, including Llama2, Llama3, and Mistral, as well as on individual models like Llama3 Instruct, Mistral Instruct, and custom fine-tuned models.
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