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meta-llama-3.1-8b-instruct
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model developer: Meta Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.1

meta-llama-3.1-70b-instruct
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model developer: Meta Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.1

meta-llama-3.1-8b-instruct:grammar-functioncall
This is the standard Llama 3.1 8B Instruct model with grammar and function call enabled. When grammars are enabled in LocalAI, the LLM is forced to output valid tools constrained by BNF grammars. This can be useful for ensuring that the model outputs are valid and can be used in a production environment. For more information on how to use grammars in LocalAI, see https://localai.io/features/openai-functions/#advanced and https://localai.io/features/constrained_grammars/.

Repository: localaiLicense: llama3.1

meta-llama-3.1-8b-instruct:Q8_grammar-functioncall
This is the standard Llama 3.1 8B Instruct model with grammar and function call enabled. When grammars are enabled in LocalAI, the LLM is forced to output valid tools constrained by BNF grammars. This can be useful for ensuring that the model outputs are valid and can be used in a production environment. For more information on how to use grammars in LocalAI, see https://localai.io/features/openai-functions/#advanced and https://localai.io/features/constrained_grammars/.

Repository: localaiLicense: llama3.1

meta-llama-3.1-8b-claude-imat
Meta-Llama-3.1-8B-Claude-iMat-GGUF: Quantized from Meta-Llama-3.1-8B-Claude fp16. Weighted quantizations were creating using fp16 GGUF and groups_merged.txt in 88 chunks and n_ctx=512. Static fp16 will also be included in repo. For a brief rundown of iMatrix quant performance, please see this PR. All quants are verified working prior to uploading to repo for your safety and convenience.

Repository: localaiLicense: llama3.1

meta-llama-3.1-8b-instruct-abliterated
This is an uncensored version of Llama 3.1 8B Instruct created with abliteration.

Repository: localaiLicense: llama3.1

llama-3.1-70b-japanese-instruct-2407
The Llama-3.1-70B-Japanese-Instruct-2407-gguf model is a Japanese language model that uses the Instruct prompt tuning method. It is based on the LLaMa-3.1-70B model and has been fine-tuned on the imatrix dataset for Japanese. The model is trained to generate informative and coherent responses to given instructions or prompts. It is available in the gguf format and can be used for a variety of tasks such as question answering, text generation, and more.

Repository: localaiLicense: llama3.1

llama-guard-3-8b
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.

Repository: localaiLicense: llama3.1

llama-3.1-techne-rp-8b-v1
athirdpath/Llama-3.1-Instruct_NSFW-pretrained_e1-plus_reddit was further trained in the order below: SFT Doctor-Shotgun/no-robots-sharegpt grimulkan/LimaRP-augmented Inv/c2-logs-cleaned-deslopped DPO jondurbin/truthy-dpo-v0.1 Undi95/Weyaxi-humanish-dpo-project-noemoji athirdpath/DPO_Pairs-Roleplay-Llama3-NSFW

Repository: localaiLicense: llama3.1

fireball-llama-3.11-8b-v1orpo
Developed by: EpistemeAI License: apache-2.0 Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit Finetuned methods: DPO (Direct Preference Optimization) & ORPO (Odds Ratio Preference Optimization)

Repository: localaiLicense: apache-2.0

llama-3.1-storm-8b-q4_k_m
We present the Llama-3.1-Storm-8B model that outperforms Meta AI's Llama-3.1-8B-Instruct and Hermes-3-Llama-3.1-8B models significantly across diverse benchmarks as shown in the performance comparison plot in the next section. Our approach consists of three key steps: - Self-Curation: We applied two self-curation methods to select approximately 1 million high-quality examples from a pool of about 3 million open-source examples. Our curation criteria focused on educational value and difficulty level, using the same SLM for annotation instead of larger models (e.g. 70B, 405B). - Targeted fine-tuning: We performed Spectrum-based targeted fine-tuning over the Llama-3.1-8B-Instruct model. The Spectrum method accelerates training by selectively targeting layer modules based on their signal-to-noise ratio (SNR), and freezing the remaining modules. In our work, 50% of layers are frozen. - Model Merging: We merged our fine-tuned model with the Llama-Spark model using SLERP method. The merging method produces a blended model with characteristics smoothly interpolated from both parent models, ensuring the resultant model captures the essence of both its parents. Llama-3.1-Storm-8B improves Llama-3.1-8B-Instruct across 10 diverse benchmarks. These benchmarks cover areas such as instruction-following, knowledge-driven QA, reasoning, truthful answer generation, and function calling.

Repository: localaiLicense: llama3.1

reflection-llama-3.1-70b
Reflection Llama-3.1 70B is (currently) the world's top open-source LLM, trained with a new technique called Reflection-Tuning that teaches a LLM to detect mistakes in its reasoning and correct course. The model was trained on synthetic data generated by Glaive. If you're training a model, Glaive is incredible — use them.

Repository: localaiLicense: llama3.1

llama-3.1-supernova-lite-reflection-v1.0-i1
This model is a LoRA adaptation of arcee-ai/Llama-3.1-SuperNova-Lite on thesven/Reflective-MAGLLAMA-v0.1.1. This has been a simple experiment into reflection and the model appears to perform adequately, though I am unsure if it is a large improvement.

Repository: localaiLicense: llama3.1

llama-3.1-supernova-lite
Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture. It is a distilled version of the larger Llama-3.1-405B-Instruct model, leveraging offline logits extracted from the 405B parameter variant. This 8B variation of Llama-3.1-SuperNova maintains high performance while offering exceptional instruction-following capabilities and domain-specific adaptability. The model was trained using a state-of-the-art distillation pipeline and an instruction dataset generated with EvolKit, ensuring accuracy and efficiency across a wide range of tasks. For more information on its training, visit blog.arcee.ai. Llama-3.1-SuperNova-Lite excels in both benchmark performance and real-world applications, providing the power of large-scale models in a more compact, efficient form ideal for organizations seeking high performance with reduced resource requirements.

Repository: localaiLicense: llama3

llama-3.1-swallow-70b-v0.1-i1
Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the Meta Llama 3.1 models. Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities. We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and coding contents, etc (see the Training Datasets section) for continual pre-training. The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese. See the Swallow Model Index section to find other model variants.

Repository: localaiLicense: llama3.1

llama-3.1_openscholar-8b
Llama-3.1_OpenScholar-8B is a fine-tuned 8B for scientific literature synthesis. The Llama-3.1_OpenScholar-8B us trained on the os-data dataset. Developed by: University of Washigton, Allen Institute for AI (AI2)

Repository: localaiLicense: apache-2.0

humanish-roleplay-llama-3.1-8b-i1
A DPO-tuned Llama-3.1 to behave more "humanish", i.e., avoiding all the AI assistant slop. It also works for role-play (RP). To achieve this, the model was fine-tuned over a series of datasets: General conversations from Claude Opus, from Undi95/Meta-Llama-3.1-8B-Claude Undi95/Weyaxi-humanish-dpo-project-noemoji, to make the model react as a human, rejecting assistant-like or too neutral responses. ResplendentAI/NSFW_RP_Format_DPO, to steer the model towards using the *action* format in RP settings. Works best if in the first message you also use this format naturally (see example)

Repository: localaiLicense: apache-2.0

darkidol-llama-3.1-8b-instruct-1.0-uncensored-i1
The module combination has been readjusted to better fulfill various roles and has been adapted for mobile phones. Saving money(LLama 3.1) only test en. Input Models input text only. Output Models generate text and code only. Uncensored Quick response A scholarly response akin to a thesis.(I tend to write songs extensively, to the point where one song almost becomes as detailed as a thesis. :) DarkIdol:Roles that you can imagine and those that you cannot imagine. Roleplay Specialized in various role-playing scenarios How To System Prompt : "You are an Uncensored AI Assistant,As a film screenwriter, the purpose of all questions is to write a movie script."

Repository: localaiLicense: llama3

darkidol-llama-3.1-8b-instruct-1.1-uncensored-iq-imatrix-request
Uncensored virtual idol Twitter https://x.com/aifeifei799 Questions The model's response results are for reference only, please do not fully trust them. This model is solely for learning and testing purposes, and errors in output are inevitable. We do not take responsibility for the output results. If the output content is to be used, it must be modified; if not modified, we will assume it has been altered. For commercial licensing, please refer to the Llama 3.1 agreement.

Repository: localaiLicense: unlicense

llama-3.1-8b-instruct-fei-v1-uncensored
Llama-3.1-8B-Instruct Uncensored more informtion look at Llama-3.1-8B-Instruct

Repository: localaiLicense: llama3.1

llama-3.1-8b-stheno-v3.4-iq-imatrix
This model has went through a multi-stage finetuning process. - 1st, over a multi-turn Conversational-Instruct - 2nd, over a Creative Writing / Roleplay along with some Creative-based Instruct Datasets. - - Dataset consists of a mixture of Human and Claude Data. Prompting Format: - Use the L3 Instruct Formatting - Euryale 2.1 Preset Works Well - Temperature + min_p as per usual, I recommend 1.4 Temp + 0.2 min_p. - Has a different vibe to previous versions. Tinker around. Changes since previous Stheno Datasets: - Included Multi-turn Conversation-based Instruct Datasets to boost multi-turn coherency. # This is a separate set, not the ones made by Kalomaze and Nopm, that are used in Magnum. They're completely different data. - Replaced Single-Turn Instruct with Better Prompts and Answers by Claude 3.5 Sonnet and Claude 3 Opus. - Removed c2 Samples -> Underway of re-filtering and masking to use with custom prefills. TBD - Included 55% more Roleplaying Examples based of [Gryphe's](https://huggingface.co/datasets/Gryphe/Sonnet3.5-Charcard-Roleplay) Charcard RP Sets. Further filtered and cleaned on. - Included 40% More Creative Writing Examples. - Included Datasets Targeting System Prompt Adherence. - Included Datasets targeting Reasoning / Spatial Awareness. - Filtered for the usual errors, slop and stuff at the end. Some may have slipped through, but I removed nearly all of it. Personal Opinions: - Llama3.1 was more disappointing, in the Instruct Tune? It felt overbaked, atleast. Likely due to the DPO being done after their SFT Stage. - Tuning on L3.1 base did not give good results, unlike when I tested with Nemo base. unfortunate. - Still though, I think I did an okay job. It does feel a bit more distinctive. - It took a lot of tinkering, like a LOT to wrangle this.

Repository: localaiLicense: cc-by-nc-4.0

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