LocalAI models

intellect-1-instruct

INTELLECT-1 is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code. This is an instruct model. The base model associated with it is INTELLECT-1. INTELLECT-1 was trained on up to 14 concurrent nodes distributed across 3 continents, with contributions from 30 independent community contributors providing compute. The training code utilizes the prime framework, a scalable distributed training framework designed for fault-tolerant, dynamically scaling, high-perfomance training on unreliable, globally distributed workers. The key abstraction that allows dynamic scaling is the ElasticDeviceMesh which manages dynamic global process groups for fault-tolerant communication across the internet and local process groups for communication within a node. The model was trained using the DiLoCo algorithms with 100 inner steps. The global all-reduce was done with custom int8 all-reduce kernels to reduce the communication payload required, greatly reducing the communication overhead by a factor 400x.

llama-3.3-70b-instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

l3.3-70b-euryale-v2.3

A direct replacement / successor to Euryale v2.2, not Hanami-x1, though it is slightly better than them in my opinion.

l3.3-ms-evayale-70b

This model was created as I liked the storytelling of EVA but the prose and details of scenes from EURYALE, my goal is to merge the robust storytelling of both models while attempting to maintain the positives of both models.

rwkv-6-world-7b

RWKV (pronounced RwaKuv) is an RNN with GPT-level LLM performance, and can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7. So it's combining the best of RNN and transformer - great performance, fast inference, fast training, saves VRAM, "infinite" ctxlen, and free text embedding. Moreover it's 100% attention-free, and a Linux Foundation AI project.

qwen2.5-coder-14b

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. Long-context Support up to 128K tokens.

qwen2.5-coder-3b-instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. Long-context Support up to 128K tokens.

qwen2.5-coder-32b-instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. Long-context Support up to 128K tokens.

qwen2.5-coder-14b-instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. Long-context Support up to 128K tokens.

qwen2.5-coder-1.5b-instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. Long-context Support up to 128K tokens.

qwen2.5-coder-7b-instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. Long-context Support up to 128K tokens.

qwen2.5-coder-7b-3x-instruct-ties-v1.2-i1

The following models were included in the merge: BenevolenceMessiah/Qwen2.5-Coder-7B-Chat-Instruct-TIES-v1.2 MadeAgents/Hammer2.0-7b huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated

qwen2.5-coder-7b-instruct-abliterated-i1

This is an uncensored version of Qwen2.5-Coder-7B-Instruct created with abliteration (see this article to know more about it). Special thanks to @FailSpy for the original code and technique. Please follow him if you're interested in abliterated models.

rombos-coder-v2.5-qwen-7b

Rombos-Coder-V2.5-Qwen-7b is a continues finetuned version of Qwen2.5-Coder-7B-Instruct. I took it upon myself to merge the instruct model with the base model myself using the * Ties* merge method as demonstrated in my own "Continuous Finetuning" method (link available). This version of the model shows higher performance than the original instruct and base models.

rombos-coder-v2.5-qwen-32b

Rombos-Coder-V2.5-Qwen-32b is a continues finetuned version of Qwen2.5-Coder-32B-Instruct. I took it upon myself to merge the instruct model with the base model myself using the Ties merge method as demonstrated in my own "Continuous Finetuning" method (link available). This version of the model shows higher performance than the original instruct and base models.

rombos-coder-v2.5-qwen-14b

Rombos-Coder-V2.5-Qwen-14b is a continues finetuned version of Qwen2.5-Coder-14B-Instruct. I took it upon myself to merge the instruct model with the base model myself using the Ties merge method as demonstrated in my own "Continuous Finetuning" method (link available). This version of the model shows higher performance than the original instruct and base models.

qwen2.5-coder-32b-instruct-uncensored-i1

The LLM model is based on sloshywings/Qwen2.5-Coder-32B-Instruct-Uncensored. It is a large language model with 32B parameters that has been fine-tuned on coding tasks and instructions.

opencoder-8b-base

The model is a quantized version of infly/OpenCoder-8B-Base created using llama.cpp. It is part of the OpenCoder LLM family which includes 1.5B and 8B base and chat models, supporting both English and Chinese languages. The original OpenCoder model was pretrained on 2.5 trillion tokens composed of 90% raw code and 10% code-related web data, and supervised finetuned on over 4.5M high-quality SFT examples. It achieves high performance across multiple language model benchmarks and is one of the most comprehensively open-sourced models available.

opencoder-8b-instruct

The LLM model is QuantFactory/OpenCoder-8B-Instruct-GGUF, which is a quantized version of infly/OpenCoder-8B-Instruct. It is created using llama.cpp and supports both English and Chinese languages. The original model, infly/OpenCoder-8B-Instruct, is pretrained on 2.5 trillion tokens composed of 90% raw code and 10% code-related web data, and supervised finetuned on over 4.5M high-quality SFT examples. It achieves high performance across multiple language model benchmarks and is one of the leading open-source models for code.

opencoder-1.5b-base

The model is a large language model with 1.5 billion parameters, trained on 2.5 trillion tokens of code-related data. It supports both English and Chinese languages and is part of the OpenCoder LLM family which also includes 8B base and chat models. The model achieves high performance across multiple language model benchmarks and is one of the most comprehensively open-sourced models available.

opencoder-1.5b-instruct

The model is a quantized version of [infly/OpenCoder-1.5B-Instruct](https://huggingface.co/infly/OpenCoder-1.5B-Instruct) created using llama.cpp. The original model, infly/OpenCoder-1.5B-Instruct, is an open and reproducible code LLM family which includes 1.5B and 8B base and chat models, supporting both English and Chinese languages. The model is pretrained on 2.5 trillion tokens composed of 90% raw code and 10% code-related web data, and supervised finetuned on over 4.5M high-quality SFT examples. It achieves high performance across multiple language model benchmarks, positioning it among the leading open-source models for code.

granite-3.0-1b-a400m-instruct

Granite 3.0 language models are a new set of lightweight state-of-the-art, open foundation models that natively support multilinguality, coding, reasoning, and tool usage, including the potential to be run on constrained compute resources. All the models are publicly released under an Apache 2.0 license for both research and commercial use. The models' data curation and training procedure were designed for enterprise usage and customization in mind, with a process that evaluates datasets for governance, risk and compliance (GRC) criteria, in addition to IBM's standard data clearance process and document quality checks. Granite 3.0 includes 4 different models of varying sizes: Dense Models: 2B and 8B parameter models, trained on 12 trillion tokens in total. Mixture-of-Expert (MoE) Models: Sparse 1B and 3B MoE models, with 400M and 800M activated parameters respectively, trained on 10 trillion tokens in total. Accordingly, these options provide a range of models with different compute requirements to choose from, with appropriate trade-offs with their performance on downstream tasks. At each scale, we release a base model — checkpoints of models after pretraining, as well as instruct checkpoints — models finetuned for dialogue, instruction-following, helpfulness, and safety.

moe-girl-800ma-3bt

A roleplay-centric finetune of IBM's Granite 3.0 3B-A800M. LoRA finetune trained locally, whereas the others were FFT; while this results in less uptake of training data, it should also mean less degradation in Granite's core abilities, making it potentially easier to use for general-purpose tasks. Disclaimer PLEASE do not expect godliness out of this, it's a model with 800 million active parameters. Expect something more akin to GPT-3 (the original, not GPT-3.5.) (Furthermore, this version is by a less experienced tuner; it's my first finetune that actually has decent-looking graphs, I don't really know what I'm doing yet!)

moe-girl-1ba-7bt-i1

A finetune of OLMoE by AllenAI designed for roleplaying (and maybe general usecases if you try hard enough). PLEASE do not expect godliness out of this, it's a model with 1 billion active parameters. Expect something more akin to Gemma 2 2B, not Llama 3 8B.

salamandra-7b-instruct

Transformer-based decoder-only language model that has been pre-trained on 7.8 trillion tokens of highly curated data. The pre-training corpus contains text in 35 European languages and code. Salamandra comes in three different sizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants. This model card corresponds to the 7B instructed version.

llama-3.2-1b-instruct:q4_k_m

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 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.

llama-3.2-3b-instruct:q4_k_m

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 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.

llama-3.2-3b-instruct:q8_0

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 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.

llama-3.2-1b-instruct:q8_0

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 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.

versatillama-llama-3.2-3b-instruct-abliterated

Small but Smart Fine-Tuned on Vast dataset of Conversations. Able to Generate Human like text with high performance within its size. It is Very Versatile when compared for it's size and Parameters and offers capability almost as good as Llama 3.1 8B Instruct.

llama3.2-3b-enigma

Enigma is a code-instruct model built on Llama 3.2 3b. It is a high quality code instruct model with the Llama 3.2 Instruct chat format. The model is finetuned on synthetic code-instruct data generated with Llama 3.1 405b and supplemented with generalist synthetic data. It uses the Llama 3.2 Instruct prompt format.

llama3.2-3b-esper2

Esper 2 is a DevOps and cloud architecture code specialist built on Llama 3.2 3b. It is an AI assistant focused on AWS, Azure, GCP, Terraform, Dockerfiles, pipelines, shell scripts and more, with real world problem solving and high quality code instruct performance within the Llama 3.2 Instruct chat format. Finetuned on synthetic DevOps-instruct and code-instruct data generated with Llama 3.1 405b and supplemented with generalist chat data.

llama-3.2-3b-agent007

The model is a quantized version of EpistemeAI/Llama-3.2-3B-Agent007, developed by EpistemeAI and fine-tuned from unsloth/llama-3.2-3b-instruct-bnb-4bit. It was trained 2x faster with Unsloth and Huggingface's TRL library. Fine tuned with Agent datasets.

llama-3.2-3b-agent007-coder

The Llama-3.2-3B-Agent007-Coder-GGUF is a quantized version of the EpistemeAI/Llama-3.2-3B-Agent007-Coder model, which is a fine-tuned version of the unsloth/llama-3.2-3b-instruct-bnb-4bit model. It is created using llama.cpp and trained with additional datasets such as the Agent dataset, Code Alpaca 20K, and magpie ultra 0.1. This model is optimized for multilingual dialogue use cases and agentic retrieval and summarization tasks. The model is available for commercial and research use in multiple languages and is best used with the transformers library.

fireball-meta-llama-3.2-8b-instruct-agent-003-128k-code-dpo

The LLM model is a quantized version of EpistemeAI/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO, which is an experimental and revolutionary fine-tune with DPO dataset to allow LLama 3.1 8B to be an agentic coder. It has some built-in agent features such as search, calculator, and ReAct. Other noticeable features include self-learning using unsloth, RAG applications, and memory. The context window of the model is 128K. It can be integrated into projects using popular libraries like Transformers and vLLM. The model is suitable for use with Langchain or LLamaIndex. The model is developed by EpistemeAI and licensed under the Apache 2.0 license.

llama-3.2-chibi-3b

Small parameter LLMs are ideal for navigating the complexities of the Japanese language, which involves multiple character systems like kanji, hiragana, and katakana, along with subtle social cues. Despite their smaller size, these models are capable of delivering highly accurate and context-aware results, making them perfect for use in environments where resources are constrained. Whether deployed on mobile devices with limited processing power or in edge computing scenarios where fast, real-time responses are needed, these models strike the perfect balance between performance and efficiency, without sacrificing quality or speed.

llama-3.2-3b-reasoning-time

Lyte/Llama-3.2-3B-Reasoning-Time is a large language model with 3.2 billion parameters, designed for reasoning and time-based tasks in English. It is based on the Llama architecture and has been quantized using the GGUF format by mradermacher.

llama-3.2-sun-2.5b-chat

Base Model Llama 3.2 1B Extended Size 1B to 2.5B parameters Extension Method Proprietary technique developed by MedIT Solutions Fine-tuning Open (or open subsets allowing for commercial use) open datasets from HF Open (or open subsets allowing for commercial use) SFT datasets from HF Training Status Current version: chat-1.0.0 Key Features Built on Llama 3.2 architecture Expanded from 1B to 2.47B parameters Optimized for open-ended conversations Incorporates supervised fine-tuning for improved performance Use Case General conversation and task-oriented interactions

llama-3.2-3b-instruct-uncensored

This is an uncensored version of the original Llama-3.2-3B-Instruct, created using mlabonne's script, which builds on FailSpy's notebook and the original work from Andy Arditi et al..

calme-3.3-llamaloi-3b

This model is an advanced iteration of the powerful meta-llama/Llama-3.2-3B, specifically fine-tuned to enhance its capabilities in French Legal domain.

calme-3.2-llamaloi-3b

This model is an advanced iteration of the powerful meta-llama/Llama-3.2-3B, specifically fine-tuned to enhance its capabilities in French Legal domain.

calme-3.1-llamaloi-3b

This model is an advanced iteration of the powerful meta-llama/Llama-3.2-3B, specifically fine-tuned to enhance its capabilities in French Legal domain.

llama3.2-3b-enigma

ValiantLabs/Llama3.2-3B-Enigma is an Enigma model built on Llama 3.2 3b. It is a high-quality code-instruct model with the Llama 3.2 Instruct chat format. The model is finetuned on synthetic code-instruct data generated using Llama 3.1 405b and supplemented with generalist synthetic data. This model is suitable for both code-instruct and general chat applications.

llama3.2-3b-shiningvaliant2-i1

Shining Valiant 2 is a chat model built on Llama 3.2 3b, finetuned on our data for friendship, insight, knowledge and enthusiasm. Finetuned on meta-llama/Llama-3.2-3B-Instruct for best available general performance Trained on a variety of high quality data; focused on science, engineering, technical knowledge, and structured reasoning Also available for Llama 3.1 70b and Llama 3.1 8b! Version This is the 2024-09-27 release of Shining Valiant 2 for Llama 3.2 3b.

llama-doctor-3.2-3b-instruct

The Llama-Doctor-3.2-3B-Instruct model is designed for text generation tasks, particularly in contexts where instruction-following capabilities are needed. This model is a fine-tuned version of the base Llama-3.2-3B-Instruct model and is optimized for understanding and responding to user-provided instructions or prompts. The model has been trained on a specialized dataset, avaliev/chat_doctor, to enhance its performance in providing conversational or advisory responses, especially in medical or technical fields.

onellm-doey-v1-llama-3.2-3b

This model is a fine-tuned version of LLaMA 3.2-3B, optimized using LoRA (Low-Rank Adaptation) on the NVIDIA ChatQA-Training-Data. It is tailored for conversational AI, question answering, and other instruction-following tasks, with support for sequences up to 1024 tokens.

llama-sentient-3.2-3b-instruct

The Llama-Sentient-3.2-3B-Instruct model is a fine-tuned version of the Llama-3.2-3B-Instruct model, optimized for text generation tasks, particularly where instruction-following abilities are critical. This model is trained on the mlabonne/lmsys-arena-human-preference-55k-sharegpt dataset, which enhances its performance in conversational and advisory contexts, making it suitable for a wide range of applications.

llama-smoltalk-3.2-1b-instruct

The Llama-SmolTalk-3.2-1B-Instruct model is a lightweight, instruction-tuned model designed for efficient text generation and conversational AI tasks. With a 1B parameter architecture, this model strikes a balance between performance and resource efficiency, making it ideal for applications requiring concise, contextually relevant outputs. The model has been fine-tuned to deliver robust instruction-following capabilities, catering to both structured and open-ended queries. Key Features: Instruction-Tuned Performance: Optimized to understand and execute user-provided instructions across diverse domains. Lightweight Architecture: With just 1 billion parameters, the model provides efficient computation and storage without compromising output quality. Versatile Use Cases: Suitable for tasks like content generation, conversational interfaces, and basic problem-solving. Intended Applications: Conversational AI: Engage users with dynamic and contextually aware dialogue. Content Generation: Produce summaries, explanations, or other creative text outputs efficiently. Instruction Execution: Follow user commands to generate precise and relevant responses.

qwen2.5-14b-instruct

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.

qwen2.5-math-7b-instruct

In August 2024, we released the first series of mathematical LLMs - Qwen2-Math - of our Qwen family. A month later, we have upgraded it and open-sourced Qwen2.5-Math series, including base models Qwen2.5-Math-1.5B/7B/72B, instruction-tuned models Qwen2.5-Math-1.5B/7B/72B-Instruct, and mathematical reward model Qwen2.5-Math-RM-72B. Unlike Qwen2-Math series which only supports using Chain-of-Thught (CoT) to solve English math problems, Qwen2.5-Math series is expanded to support using both CoT and Tool-integrated Reasoning (TIR) to solve math problems in both Chinese and English. The Qwen2.5-Math series models have achieved significant performance improvements compared to the Qwen2-Math series models on the Chinese and English mathematics benchmarks with CoT. The base models of Qwen2-Math are initialized with Qwen2-1.5B/7B/72B, and then pretrained on a meticulously designed Mathematics-specific Corpus. This corpus contains large-scale high-quality mathematical web texts, books, codes, exam questions, and mathematical pre-training data synthesized by Qwen2.

qwen2.5-14b_uncencored

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Uncensored qwen2.5

qwen2.5-coder-7b-instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). For Qwen2.5-Coder, we release three base language models and instruction-tuned language models, 1.5, 7 and 32 (coming soon) billion parameters. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. Long-context Support up to 128K tokens.

qwen2.5-math-72b-instruct

In August 2024, we released the first series of mathematical LLMs - Qwen2-Math - of our Qwen family. A month later, we have upgraded it and open-sourced Qwen2.5-Math series, including base models Qwen2.5-Math-1.5B/7B/72B, instruction-tuned models Qwen2.5-Math-1.5B/7B/72B-Instruct, and mathematical reward model Qwen2.5-Math-RM-72B. Unlike Qwen2-Math series which only supports using Chain-of-Thught (CoT) to solve English math problems, Qwen2.5-Math series is expanded to support using both CoT and Tool-integrated Reasoning (TIR) to solve math problems in both Chinese and English. The Qwen2.5-Math series models have achieved significant performance improvements compared to the Qwen2-Math series models on the Chinese and English mathematics benchmarks with CoT

qwen2.5-0.5b-instruct

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.

qwen2.5-1.5b-instruct

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.

qwen2.5-32b

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.

qwen2.5-32b-instruct

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.

qwen2.5-72b-instruct

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.

bigqwen2.5-52b-instruct

BigQwen2.5-52B-Instruct is a Qwen/Qwen2-32B-Instruct self-merge made with MergeKit. It applies the mlabonne/Meta-Llama-3-120B-Instruct recipe.

replete-llm-v2.5-qwen-14b

Replete-LLM-V2.5-Qwen-14b is a continues finetuned version of Qwen2.5-14B. I noticed recently that the Qwen team did not learn from my methods of continuous finetuning, the great benefits, and no downsides of it. So I took it upon myself to merge the instruct model with the base model myself using the Ties merge method This version of the model shows higher performance than the original instruct and base models.

replete-llm-v2.5-qwen-7b

Replete-LLM-V2.5-Qwen-7b is a continues finetuned version of Qwen2.5-14B. I noticed recently that the Qwen team did not learn from my methods of continuous finetuning, the great benefits, and no downsides of it. So I took it upon myself to merge the instruct model with the base model myself using the Ties merge method This version of the model shows higher performance than the original instruct and base models.

calme-2.2-qwen2.5-72b-i1

This model is a fine-tuned version of the powerful Qwen/Qwen2.5-72B-Instruct, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications. Use Cases This model is suitable for a wide range of applications, including but not limited to: Advanced question-answering systems Intelligent chatbots and virtual assistants Content generation and summarization Code generation and analysis Complex problem-solving and decision support

t.e-8.1-iq-imatrix-request

Trained for roleplay uses.

rombos-llm-v2.5.1-qwen-3b

Rombos-LLM-V2.5.1-Qwen-3b is a little experiment that merges a high-quality LLM, arcee-ai/raspberry-3B, using the last step of the Continuous Finetuning method outlined in a Google document. The merge is done using the mergekit with the following parameters: - Models: Qwen2.5-3B-Instruct, raspberry-3B - Merge method: ties - Base model: Qwen2.5-3B - Parameters: weight=1, density=1, normalize=true, int8_mask=true - Dtype: bfloat16 The model has been evaluated on various tasks and datasets, and the results are available on the Open LLM Leaderboard. The model has shown promising performance across different benchmarks.

qwen2.5-7b-ins-v3

Qwen 2.5 fine-tuned on CoT to match o1 performance. An attempt to build an Open o1 mathcing OpenAI o1 model Demo: https://huggingface.co/spaces/happzy2633/open-o1

supernova-medius

Arcee-SuperNova-Medius is a 14B parameter language model developed by Arcee.ai, built on the Qwen2.5-14B-Instruct architecture. This unique model is the result of a cross-architecture distillation pipeline, combining knowledge from both the Qwen2.5-72B-Instruct model and the Llama-3.1-405B-Instruct model. By leveraging the strengths of these two distinct architectures, SuperNova-Medius achieves high-quality instruction-following and complex reasoning capabilities in a mid-sized, resource-efficient form. SuperNova-Medius is designed to excel in a variety of business use cases, including customer support, content creation, and technical assistance, while maintaining compatibility with smaller hardware configurations. It’s an ideal solution for organizations looking for advanced capabilities without the high resource requirements of larger models like our SuperNova-70B.

eva-qwen2.5-14b-v0.1-i1

A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-14B on mixture of synthetic and natural data. It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and "flavor" of the resulting model.

cursorcore-qw2.5-7b-i1

CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read our paper to learn more.

cursorcore-qw2.5-1.5b-lc-i1

CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read our paper to learn more.

edgerunner-command-nested-i1

EdgeRunner-Command-Nested is an advanced large language model designed specifically for handling complex nested function calls. Initialized from Qwen2.5-7B-Instruct, further enhanced by the integration of the Hermes function call template and additional training on a specialized dataset (based on TinyAgent). This extra dataset focuses on personal domain applications, providing the model with a robust understanding of nested function scenarios that are typical in complex user interactions.

tsunami-0.5x-7b-instruct-i1

TSUNAMI: Transformative Semantic Understanding and Natural Augmentation Model for Intelligence. TSUNAMI full name was created by ChatGPT. infomation Tsunami-0.5x-7B-Instruct is Thai Large Language Model that fine-tuned from Qwen2.5-7B around 100,000 rows in Thai dataset.

qevacot-7b-v2

This model was merged using the TIES merge method using Qwen/Qwen2.5-7B as a base. The following models were included in the merge: c10x/CoT-2.5 EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1 huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2 Cran-May/T.E-8.1

meissa-qwen2.5-7b-instruct

Meissa is designated Lambda Orionis, forms Orion's head, and is a multiple star with a combined apparent magnitude of 3.33. Its name means the "shining one". This model is fine tuned over writing and role playing datasets (maybe the first on qwen2.5-7b), aiming to enhance model's performance in novel writing and roleplaying. The model is fine-tuned over Orion-zhen/Qwen2.5-7B-Instruct-Uncensored

thebeagle-v2beta-32b-mgs

This model is an experimental version of our latest innovation: MGS. Its up to you to figure out what does it means, but its very explicit. We didn't applied our known UNA algorithm to the forward pass, but they are entirely compatible and operates in different parts of the neural network and in different ways, tho they both can be seen as a regularization technique. Updated tokenizer_config.json (from the base_model) Regenerated Quants (being uploaded) Re-submitted Leaderboard Evaluation, MATH & IFeval have relevant updates Aligned LICENSE with Qwen terms. MGS stands for... Many-Geeks-Searching... and thats it. Hint: 1+1 is 2, and 1+1 is not 3 We still believe on 1-Epoch should be enough, so we just did 1 Epoch only. Dataset Used here the first decent (corpora & size) dataset on the hub: Magpie-Align/Magpie-Pro-300K-Filtered Kudos to the Magpie team to contribute with some decent stuff that I personally think is very good to ablate. It achieves the following results on the evaluation set: Loss: 0.5378 (1 Epoch), outperforming the baseline model.

meraj-mini

Arcee Meraj Mini is a quantized version of the Meraj-Mini model, created using llama.cpp. It is an open-source model that is fine-tuned from the Qwen2.5-7B-Instruct model and is designed for both Arabic and English languages. The model has undergone evaluations across multiple benchmarks in both languages and demonstrates top-tier performance in Arabic and competitive results in English. The key stages in its development include data preparation, initial training, iterative training and post-training, evaluation, and final model creation. The model is capable of solving a wide range of language tasks and is suitable for various applications such as education, mathematics and coding, customer service, and content creation. The Arcee Meraj Mini model consistently outperforms state-of-the-art models on most benchmarks of the Open Arabic LLM Leaderboard (OALL), highlighting its improvements and effectiveness in Arabic language content.

spiral-da-hyah-qwen2.5-72b-i1

Model stock merge for fun. This model was merged using the Model Stock merge method using rombodawg/Rombos-LLM-V2.5-Qwen-72b as a base. The following models were included in the merge: - anthracite-org/magnum-v4-72b - AXCXEPT/EZO-Qwen2.5-72B-Instruct

whiterabbitneo-2.5-qwen-2.5-coder-7b

WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. Models are now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI.

cybertron-v4-qw7b-mgs

Here we use our novel approach called MGS. Its up to you to figure out what it means. Cybertron V4 went thru SFT over Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1

q25-1.5b-veolu

Q25-1.5B-Veo Lu is a tiny General-Purpose Creative model, made up of a merge of bespoke finetunes on Qwen 2.5-1.5B-Instruct. Inspired by the success of MN-12B-Mag Mell and MS-Meadowlark-22B, Veo Lu was trained on a healthy, balanced diet of of Internet fiction, roleplaying, adventuring, and reasoning/general knowledge. The components of Veo Lu are: Bard (pretrain, writing): Fujin (Cleaned/extended Rosier) Scribe (pretrain, roleplay): Creative Writing Multiturn Cartographer (pretrain, adventuring): SpringDragon Alchemist (SFT, science/reasoning): ScienceQA, MedquadQA, Orca Math Word Problems This model is capable of carrying on a scene without going completely off the rails. That being said, it only has 1.5B parameters. So please, for the love of God, manage your expectations. Since it's Qwen, use ChatML formatting. Turn the temperature down to ~0.7-0.8 and try a dash of rep-pen.

llenn-v0.75-qwen2.5-72b-i1

The following models were included in the merge: rombodawg/Rombos-LLM-V2.5-Qwen-72b abacusai/Dracarys2-72B-Instruct EVA-UNIT-01/EVA-Qwen2.5-72B-v0.0 ZeusLabs/Chronos-Platinum-72B m8than/banana-2-b-72b

eva-qwen2.5-14b-v0.2

A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-14B on mixture of synthetic and natural data. It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and "flavor" of the resulting model. Version notes for 0.2: Now using the refined dataset from 32B 0.2. Major improvements in coherence, instruction following and long-context comprehension over 14B v0.1. Prompt format is ChatML.

tissint-14b-128k-rp

The model is based on SuperNova-Medius (as the current best 14B model) with a 128k context with an emphasis on creativity, including NSFW and multi-turn conversations.

tq2.5-14b-sugarquill-v1

A continued pretrain of SuperNova-Medius on assorted short story data from the web. Supernova already had a nice prose, but diversifying it a bit definitely doesn't hurt. Also, finally a storywriter model with enough context for something more than a short story, that's also nice. It's a fair bit more temperamental than Gemma, but can be tamed with some sampling. Instruction following also stayed rather strong, so it works for both RP and storywriting, both in chat mode via back-and-forth co-writing and on raw completion. Overall, I'd say it successfully transfers the essence of what I liked about Gemma Sugarquill. I will also make a Qwen version of Aletheia, but with a brand new LoRA, based on a brand new RP dataset that's in the making right now. Model was trained by Auri.

calme-3.3-baguette-3b

This model is an advanced iteration of the powerful Qwen/Qwen2.5-3B, fine-tuned specifically to enhance its capabilities across general domains in both French and English.

calme-3.2-baguette-3b

This model is an advanced iteration of the powerful Qwen/Qwen2.5-3B, fine-tuned specifically to enhance its capabilities across general domains in both French and English.

calme-3.1-baguette-3b

This model is an advanced iteration of the powerful Qwen/Qwen2.5-3B, fine-tuned specifically to enhance its capabilities across general domains in both French and English.

calme-3.3-qwenloi-3b

This model is an advanced iteration of the powerful Qwen/Qwen2.5-3B, specifically fine-tuned to enhance its capabilities in French Legal domain.

calme-3.2-qwenloi-3b

This model is an advanced iteration of the powerful Qwen/Qwen2.5-3B, specifically fine-tuned to enhance its capabilities in French Legal domain.

calme-3.1-qwenloi-3b

This model is an advanced iteration of the powerful Qwen/Qwen2.5-3B, specifically fine-tuned to enhance its capabilities in French Legal domain.

eva-qwen2.5-72b-v0.1-i1

A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-72B on mixture of synthetic and natural data. It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and "flavor" of the resulting model. Dedicated to Nev. Version notes for 0.1: Reprocessed dataset (via Cahvay for 32B 0.2, used here as well), readjusted training config for 8xH100 SXM. Significant improvements in instruction following, long context understanding and overall coherence over v0.0.

celestial-harmony-14b-v1.0-experimental-1016-i1

Yet Another merge, this one for AuriAetherwiing, at their request. This is a merge of pre-trained language models created using mergekit. The following models were included in the merge: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1 v000000/Qwen2.5-Lumen-14B arcee-ai/SuperNova-Medius

qwen2.5-32b-arliai-rpmax-v1.3

RPMax is a series of models that are trained on a diverse set of curated creative writing and RP datasets with a focus on variety and deduplication. This model is designed to be highly creative and non-repetitive by making sure no two entries in the dataset have repeated characters or situations, which makes sure the model does not latch on to a certain personality and be capable of understanding and acting appropriately to any characters or situations. Many RPMax users mentioned that these models does not feel like any other RP models, having a different writing style and generally doesn't feel in-bred.

q2.5-ms-mistoria-72b-i1

This model is my fist attempt at a 72b model as usual my goal is to merge the robust storytelling of mutiple models while attempting to maintain intelligence. Merge of: - model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.1 - model: ZeusLabs/Chronos-Platinum-72B - model: shuttleai/shuttle-3

athene-v2-agent

Athene-V2-Agent is an open-source Agent LLM that surpasses the state-of-the-art in function calling and agentic capabilities. 💪 Versatile Agent Capability: Athene-V2-Agent is an agent model, capable of operating in environments with deeply nested dependencies with the environment. It is capable of reasoning and doing planning for trajectories with many tool calls necessary to answer a single query. 📊 Performance Highlights: Athene-V2-Agent surpasses GPT-4o in single FC tasks by 18% in function calling success rates, and by 17% in Agentic success rates. 🔧 Generalization to the Unseen: Athene-V2-Agent has never been trained on the functions or agentic settings used in evaluation.

athene-v2-chat

We introduce Athene-V2-Chat-72B, an open-weights LLM on-par with GPT-4o across benchmarks. It is trained through RLHF with Qwen-2.5-72B-Instruct as base model. Athene-V2-Chat-72B excels in chat, math, and coding. Its sister model, Athene-V2-Agent-72B, surpasses GPT-4o in complex function calling and agentic applications.

qwen2.5-7b-nerd-uncensored-v1.7

Model created by analyzing and selecting the optimal layers from other Qwen2.5-7B models based on their dimensional utilization efficiency, measured by the Normalized Effective Rank (NER). Computed like: Input: Weight matrix for each model layer Compute singular values σᵢ where σᵢ ≥ 0 # σᵢ represents the importance of each dimension Filter values above numerical threshold (>1e-12) Sum all singular values: S = Σσᵢ # S acts as normalization factor Create probability distribution: pᵢ = σᵢ/S # converts singular values to probabilities summing to 1 Compute Shannon entropy: H = -Σ(pᵢ * log₂(pᵢ)) # measures information content Calculate maximum possible entropy: H_max = log₂(n) Final NER score = H/H_max # normalizes score to [0,1] range Results in value between 0 and 1 for each model layer

evathene-v1.0

This 72B parameter model is a merge of Nexusflow/Athene-V2-Chat with EVA-UNIT-01/EVA-Qwen2.5-72B-v0.1. See the merge recipe below for details. This model is uncensored. You are responsible for whatever you do with it. 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.

miniclaus-qw1.5b-unamgs

Trained with Magpie-Align/Magpie-Pro-MT-300K-v0.1 Using MGS & UNA (MLP) on this tiny but powerful model.

qwen2.5-3b-smart-i1

This model was merged using the passthrough merge method using bunnycore/Qwen2.5-3B-RP-Mix + bunnycore/Qwen2.5-3b-Smart-lora_model as a base.

steyrcannon-0.2-qwen2.5-72b

SteyrCannon-0.2 is an updated revision from the original SteyrCannon. This uses EVA-Qwen2.5-72B-v0.2. Nothing else has changed.This model was merged using the TIES merge method using EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 as a base. The following models were included in the merge: anthracite-org/magnum-v4-72b EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2

q2.5-ms-mistoria-72b-v2

This model is my second attempt at a 72b model, as usual, my goal is to merge the robust storytelling of mutiple models while attempting to maintain intelligence. models: - model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 - model: ZeusLabs/Chronos-Platinum-72B - model: shuttleai/shuttle-3

eva-qwen2.5-72b-v0.2

A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-72B on mixture of synthetic and natural data. It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and "flavor" of the resulting model. Version notes for 0.2: Optimized training hyperparameters and increased sequence length. Better instruction following deeper into context and less repetition.

qwq-32b-preview

QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. As a preview release, it demonstrates promising analytical abilities while having several important limitations: Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity. Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer. Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it. Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding.

q2.5-32b-slush-i1

Slush is a two-stage model trained with high LoRA dropout, where stage 1 is a pretraining continuation on the base model, aimed at boosting the model's creativity and writing capabilities. This is then merged into the instruction tune model, and stage 2 is a fine tuning step on top of this to further enhance its roleplaying capabilities and/or to repair any damage caused in the stage 1 merge. This is still early stage. As always, feedback is welcome, and begone if you demand perfection. The second stage, like the Sunfall series, follows the Silly Tavern preset (ChatML), so ymmv in particular if you use some other tool and/or preset.

qwestion-24b

This model was merged using the DARE TIES merge method using Qwen/Qwen2.5-14B as a base. The following models were included in the merge: allknowingroger/Qwenslerp2-14B rombodawg/Rombos-LLM-V2.6-Qwen-14b VAGOsolutions/SauerkrautLM-v2-14b-DPO CultriX/Qwen2.5-14B-Wernicke

teleut-7b

A replication attempt of Tulu 3 on the Qwen 2.5 base models.

qwen2.5-7b-homercreative-mix

ZeroXClem/Qwen2.5-7B-HomerCreative-Mix is an advanced language model meticulously crafted by merging four pre-trained models using the powerful mergekit framework. This fusion leverages the Model Stock merge method to combine the creative prowess of Qandora, the instructive capabilities of Qwen-Instruct-Fusion, the sophisticated blending of HomerSlerp1, and the foundational conversational strengths of Homer-v0.5-Qwen2.5-7B. The resulting model excels in creative text generation, contextual understanding, and dynamic conversational interactions. 🚀 Merged Models This model merge incorporates the following: bunnycore/Qandora-2.5-7B-Creative: Specializes in creative text generation, enhancing the model's ability to produce imaginative and diverse content. bunnycore/Qwen2.5-7B-Instruct-Fusion: Focuses on instruction-following capabilities, improving the model's performance in understanding and executing user commands. allknowingroger/HomerSlerp1-7B: Utilizes spherical linear interpolation (SLERP) to blend model weights smoothly, ensuring a harmonious integration of different model attributes. newsbang/Homer-v0.5-Qwen2.5-7B: Acts as the foundational conversational model, providing robust language comprehension and generation capabilities.

cybercore-qwen-2.1-7b

This model was merged using the TIES merge method using rombodawg/Rombos-LLM-V2.5-Qwen-7b as a base. Models Merged fblgit/cybertron-v4-qw7B-UNAMGS + bunnycore/Qwen-2.1-7b-Persona-lora_model fblgit/cybertron-v4-qw7B-MGS + bunnycore/Qwen-2.1-7b-Persona-lora_model

homercreativeanvita-mix-qw7b

This model is currently ranked #1 on the Open LLM Leaderboard among models up to 13B parameters! Merge Method This model was merged using the SLERP merge method. Models Merged The following models were included in the merge: ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix ZeroXClem/Qwen2.5-7B-HomerCreative-Mix

math-iio-7b-instruct

The Math IIO 7B Instruct is a fine-tuned language model based on the robust Qwen2.5-7B-Instruct architecture. This model has been specifically trained to excel in single-shot mathematical reasoning and instruction-based tasks, making it a reliable choice for educational, analytical, and problem-solving applications. Key Features: Math-Optimized Capabilities: The model is designed to handle complex mathematical problems, step-by-step calculations, and reasoning tasks. Instruction-Tuned: Fine-tuned for better adherence to structured queries and task-oriented prompts, enabling clear and concise outputs. Large Vocabulary: Equipped with an extensive tokenizer configuration and custom tokens to ensure precise mathematical notation support.

virtuoso-small

Virtuoso-Small is the debut public release of the Virtuoso series of models by Arcee.ai, designed to bring cutting-edge generative AI capabilities to organizations and developers in a compact, efficient form. With 14 billion parameters, Virtuoso-Small is an accessible entry point for high-quality instruction-following, complex reasoning, and business-oriented generative AI tasks.

qwen2.5-7b-homeranvita-nerdmix

ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix is an advanced language model meticulously crafted by merging five pre-trained models using the powerful mergekit framework. This fusion leverages the Model Stock merge method to combine the creative prowess of Qandora, the instructive capabilities of Qwen-Instruct-Fusion, the sophisticated blending of HomerSlerp1, the mathematical precision of Cybertron-MGS, and the uncensored expertise of Qwen-Nerd. The resulting model excels in creative text generation, contextual understanding, technical reasoning, and dynamic conversational interactions.

qwen2.5-math-14b-instruct

This Qwen 2.5 model was trained 2x faster with Unsloth and Huggingface's TRL library. Fine-tuned it for 400 steps on garage-bAInd/Open-Platypus with a batch size of 3.

sailor2-1b-chat

Sailor2 is a community-driven initiative that brings cutting-edge multilingual language models to South-East Asia (SEA). Our research highlights a strong demand for models in the 8B and 20B parameter range for production use, alongside 1B models for specialized applications, such as speculative decoding and research purposes. These models, released under the Apache 2.0 license, provide enhanced accessibility to advanced language technologies across the region. Sailor2 builds upon the foundation of the awesome multilingual model Qwen 2.5 and is continuously pre-trained on 500B tokens to support 15 languages better with a unified model. These languages include English, Chinese, Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. By addressing the growing demand for diverse, robust, and accessible language models, Sailor2 seeks to serve the underserved in SEA areas with open, inclusive, and accessible multilingual LLMs. The Sailor2 model comes in three sizes, 1B, 8B, and 20B, which are expanded from the Qwen2.5 base models of 0.5B, 7B, and 14B, respectively.

sailor2-8b-chat

Sailor2 is a community-driven initiative that brings cutting-edge multilingual language models to South-East Asia (SEA). Our research highlights a strong demand for models in the 8B and 20B parameter range for production use, alongside 1B models for specialized applications, such as speculative decoding and research purposes. These models, released under the Apache 2.0 license, provide enhanced accessibility to advanced language technologies across the region. Sailor2 builds upon the foundation of the awesome multilingual model Qwen 2.5 and is continuously pre-trained on 500B tokens to support 15 languages better with a unified model. These languages include English, Chinese, Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. By addressing the growing demand for diverse, robust, and accessible language models, Sailor2 seeks to serve the underserved in SEA areas with open, inclusive, and accessible multilingual LLMs. The Sailor2 model comes in three sizes, 1B, 8B, and 20B, which are expanded from the Qwen2.5 base models of 0.5B, 7B, and 14B, respectively.

sailor2-20b-chat

Sailor2 is a community-driven initiative that brings cutting-edge multilingual language models to South-East Asia (SEA). Our research highlights a strong demand for models in the 8B and 20B parameter range for production use, alongside 1B models for specialized applications, such as speculative decoding and research purposes. These models, released under the Apache 2.0 license, provide enhanced accessibility to advanced language technologies across the region. Sailor2 builds upon the foundation of the awesome multilingual model Qwen 2.5 and is continuously pre-trained on 500B tokens to support 15 languages better with a unified model. These languages include English, Chinese, Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. By addressing the growing demand for diverse, robust, and accessible language models, Sailor2 seeks to serve the underserved in SEA areas with open, inclusive, and accessible multilingual LLMs. The Sailor2 model comes in three sizes, 1B, 8B, and 20B, which are expanded from the Qwen2.5 base models of 0.5B, 7B, and 14B, respectively.

72b-qwen2.5-kunou-v1

I do not really have anything planned for this model other than it being a generalist, and Roleplay Model? It was just something made and planned in minutes. Same with the 14 and 32B version. Kunou's the name of an OC I worked on for a couple of years, for a... fanfic. mmm... A kind-of successor to L3-70B-Euryale-v2.2 in all but name? I'm keeping Stheno/Euryale lineage to Llama series for now. I had a version made on top of Nemotron, a supposed Euryale 2.4 but that flopped hard, it was not my cup of tea. This version is basically a better, more cleaned up Dataset used on Euryale and Stheno.

evathene-v1.3

This 72B parameter model is a merge of sophosympatheia/Evathene-v1.1 and sophosympatheia/Evathene-v1.2. See the merge recipe below for details.

arch-function-1.5b

The Katanemo Arch-Function collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for function calling tasks. The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial. In summary, the Katanemo Arch-Function collection demonstrates: State-of-the-art performance in function calling Accurate parameter identification and suggestion, even in ambiguous or incomplete inputs High generalization across multiple function calling use cases, from API interactions to automated backend tasks. Optimized low-latency, high-throughput performance, making it suitable for real-time, production environments.

arch-function-7b

The Katanemo Arch-Function collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for function calling tasks. The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial. In summary, the Katanemo Arch-Function collection demonstrates: State-of-the-art performance in function calling Accurate parameter identification and suggestion, even in ambiguous or incomplete inputs High generalization across multiple function calling use cases, from API interactions to automated backend tasks. Optimized low-latency, high-throughput performance, making it suitable for real-time, production environments.

arch-function-3b

The Katanemo Arch-Function collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for function calling tasks. The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial. In summary, the Katanemo Arch-Function collection demonstrates: State-of-the-art performance in function calling Accurate parameter identification and suggestion, even in ambiguous or incomplete inputs High generalization across multiple function calling use cases, from API interactions to automated backend tasks. Optimized low-latency, high-throughput performance, making it suitable for real-time, production environments.

smollm-1.7b-instruct

SmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are pre-trained on SmolLM-Corpus, a curated collection of high-quality educational and synthetic data designed for training LLMs. For further details, we refer to our blogpost. To build SmolLM-Instruct, we finetuned the base models on publicly available datasets.

smollm2-1.7b-instruct

SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.

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.

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.

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/.

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/.

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.

meta-llama-3.1-8b-instruct-abliterated

This is an uncensored version of Llama 3.1 8B Instruct created with abliteration.

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.

openbuddy-llama3.1-8b-v22.1-131k

OpenBuddy - Open Multilingual Chatbot

llama3.1-8b-fireplace2

Fireplace 2 is a chat model, adding helpful structured outputs to Llama 3.1 8b Instruct. an expansion pack of supplementary outputs - request them at will within your chat: Inline function calls SQL queries JSON objects Data visualization with matplotlib Mix normal chat and structured outputs within the same conversation. Fireplace 2 supplements the existing strengths of Llama 3.1, providing inline capabilities within the Llama 3 Instruct format. Version This is the 2024-07-23 release of Fireplace 2 for Llama 3.1 8b. We're excited to bring further upgrades and releases to Fireplace 2 in the future. Help us and recommend Fireplace 2 to your friends!

sekhmet_aleph-l3.1-8b-v0.1-i1

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.

l3.1-8b-llamoutcast-i1

Warning: this model is utterly cursed. Llamoutcast 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. It responds to Llama-3 Instruct formatting.

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.

genius-llama3.1-i1

Finetuned Llama-3.1 base on Lex Fridman's podcast transcript.

llama3.1-8b-chinese-chat

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

"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.

meta-llama-3.1-instruct-9.99b-brainstorm-10x-form-3

The Meta-Llama-3.1-8B Instruct model is a large language model trained on a diverse range of text data, with the goal of generating high-quality and coherent text in response to user input. This model is enhanced through a process called "Brainstorm", which involves expanding and recalibrating the model's reasoning center to improve its creative and generative capabilities. The resulting model is capable of generating detailed, vivid, and nuanced text, with a focus on prose quality, conceptually complex responses, and a deeper understanding of the user's intent. The Brainstorm process is designed to enhance the model's performance in creative writing, roleplaying, and story generation, and to improve its ability to generate coherent and engaging text in a wide range of contexts. The model is based on the Llama3 architecture and has been fine-tuned using the Instruct framework, which provides it with a strong foundation for understanding natural language instructions and generating appropriate responses. The model can be used for a variety of tasks, including creative writing,Generating coherent and detailed text, exploring different perspectives and scenarios, and brainstorming ideas.

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

llama-spark

Llama-Spark is a powerful conversational AI model developed by Arcee.ai. It's built on the foundation of Llama-3.1-8B and merges the power of our Tome Dataset with Llama-3.1-8B-Instruct, resulting in a remarkable conversationalist that punches well above its 8B parameter weight class.

l3.1-70b-glitz-v0.2-i1

this is an experimental l3.1 70b finetuning run... that crashed midway through. however, the results are still interesting, so i wanted to publish them :3

calme-2.3-legalkit-8b-i1

This model is an advanced iteration of the powerful meta-llama/Meta-Llama-3.1-8B-Instruct, specifically fine-tuned to enhance its capabilities in the legal domain. The fine-tuning process utilized a synthetically generated dataset derived from the French LegalKit, a comprehensive legal language resource. To create this specialized dataset, I used the NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO model in conjunction with Hugging Face's Inference Endpoint. This approach allowed for the generation of high-quality, synthetic data that incorporates Chain of Thought (CoT) and advanced reasoning in its responses. The resulting model combines the robust foundation of Llama-3.1-8B with tailored legal knowledge and enhanced reasoning capabilities. This makes it particularly well-suited for tasks requiring in-depth legal analysis, interpretation, and application of French legal concepts.

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)

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.

hubble-4b-v1

Equipped with his five senses, man explores the universe around him and calls the adventure 'Science'. This is a finetune of Nvidia's Llama 3.1 4B Minitron - a shrunk down model of Llama 3.1 8B 128K.

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.

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.

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.

llama3.1-8b-shiningvaliant2

Shining Valiant 2 is a chat model built on Llama 3.1 8b, finetuned on our data for friendship, insight, knowledge and enthusiasm. Finetuned on meta-llama/Meta-Llama-3.1-8B-Instruct for best available general performance Trained on a variety of high quality data; focused on science, engineering, technical knowledge, and structured reasoning

nightygurps-14b-v1.1

This model works with Russian only. This model is designed to run GURPS roleplaying games, as well as consult and assist. This model was trained on an augmented dataset of the GURPS Basic Set rulebook. Its primary purpose was initially to become an assistant consultant and assistant Game Master for the GURPS roleplaying system, but it can also be used as a GM for running solo games as a player.

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.

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)

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)

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."

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.

llama-3.1-8b-instruct-fei-v1-uncensored

Llama-3.1-8B-Instruct Uncensored more informtion look at Llama-3.1-8B-Instruct

lumimaid-v0.2-8b

This model is based on: Meta-Llama-3.1-8B-Instruct Wandb: https://wandb.ai/undis95/Lumi-Llama-3-1-8B?nw=nwuserundis95 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!

lumimaid-v0.2-70b-i1

This model is based on: Meta-Llama-3.1-8B-Instruct Wandb: https://wandb.ai/undis95/Lumi-Llama-3-1-8B?nw=nwuserundis95 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!

l3.1-8b-celeste-v1.5

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.

kumiho-v1-rp-uwu-8b

Meet Kumiho-V1 uwu. Kumiho-V1-rp-UwU aims to be a generalist model with specialization in roleplay and writing capabilities. It is finetuned and merged with various models, with a heavy base of Meta's LLaMA 3.1-8B as base model, and Claude 3.5 Sonnet and Claude 3 Opus generated synthetic data.

infinity-instruct-7m-gen-llama3_1-70b

Infinity-Instruct-7M-Gen-Llama3.1-70B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on Infinity-Instruct-7M and Infinity-Instruct-Gen and showing favorable results on AlpacaEval 2.0 and arena-hard compared to GPT4.

cathallama-70b

Notable Performance 9% overall success rate increase on MMLU-PRO over LLaMA 3.1 70b Strong performance in MMLU-PRO categories overall Great performance during manual testing Creation workflow Models merged meta-llama/Meta-Llama-3.1-70B-Instruct turboderp/Cat-Llama-3-70B-instruct Nexusflow/Athene-70B

mahou-1.3-llama3.1-8b

Mahou is designed to provide short messages in a conversational context. It is capable of casual conversation and character roleplay.

azure_dusk-v0.2-iq-imatrix

"Following up on Crimson_Dawn-v0.2 we have Azure_Dusk-v0.2! Training on Mistral-Nemo-Base-2407 this time I've added significantly more data, as well as trained using RSLoRA as opposed to regular LoRA. Another key change is training on ChatML as opposed to Mistral Formatting." by Author.

l3.1-8b-niitama-v1.1-iq-imatrix

GGUF-IQ-Imatrix quants for Sao10K/L3.1-8B-Niitama-v1.1 Here's the subjectively superior L3 version: L3-8B-Niitama-v1 An experimental model using experimental methods. More detail on it: Tamamo and Niitama are made from the same data. Literally. The only thing that's changed is how theyre shuffled and formatted. Yet, I get wildly different results. Interesting, eh? Feels kinda not as good compared to the l3 version, but it's aight.

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 seperate 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.

llama-3.1-8b-arliai-rpmax-v1.1

RPMax is a series of models that are trained on a diverse set of curated creative writing and RP datasets with a focus on variety and deduplication. This model is designed to be highly creative and non-repetitive by making sure no two entries in the dataset have repeated characters or situations, which makes sure the model does not latch on to a certain personality and be capable of understanding and acting appropriately to any characters or situations.

violet_twilight-v0.2-iq-imatrix

Now for something a bit different, Violet_Twilight-v0.2! This model is a SLERP merge of Azure_Dusk-v0.2 and Crimson_Dawn-v0.2!

dans-personalityengine-v1.0.0-8b

This model is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline. It has been trained on a wide array of one shot instructions, multi turn instructions, role playing scenarios, text adventure games, co-writing, and much more. The full dataset is publicly available and can be found in the datasets section of the model page. There has not been any form of harmfulness alignment done on this model, please take the appropriate precautions when using it in a production environment.

nihappy-l3.1-8b-v0.09

The model is a quantized version of Arkana08/NIHAPPY-L3.1-8B-v0.09 created using llama.cpp. It is a role-playing model that integrates the finest qualities of various pre-trained language models, focusing on dynamic storytelling.

llama3.1-flammades-70b

nbeerbower/Llama3.1-Gutenberg-Doppel-70B finetuned on flammenai/Date-DPO-NoAsterisks and jondurbin/truthy-dpo-v0.1.

llama3.1-gutenberg-doppel-70b

mlabonne/Hermes-3-Llama-3.1-70B-lorablated finetuned on jondurbin/gutenberg-dpo-v0.1 and nbeerbower/gutenberg2-dpo.

llama-3.1-8b-arliai-formax-v1.0-iq-arm-imatrix

Quants for ArliAI/Llama-3.1-8B-ArliAI-Formax-v1.0. "Formax is a model that specializes in following response format instructions. Tell it the format of it's response and it will follow it perfectly. Great for data processing and dataset creation tasks." "It is also a highly uncensored model that will follow your instructions very well."

hermes-3-llama-3.1-70b-lorablated

This is an uncensored version of NousResearch/Hermes-3-Llama-3.1-70B using lorablation. The recipe is based on @grimjim's grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter (special thanks): Extraction: We extract a LoRA adapter by comparing two models: a censored Llama 3 (meta-llama/Meta-Llama-3-70B-Instruct) and an abliterated Llama 3.1 (failspy/Meta-Llama-3.1-70B-Instruct-abliterated). Merge: We merge this new LoRA adapter using task arithmetic to the censored NousResearch/Hermes-3-Llama-3.1-70B to abliterate it.

hermes-3-llama-3.1-8b-lorablated

This is an uncensored version of NousResearch/Hermes-3-Llama-3.1-8B using lorablation. The recipe is simple: Extraction: We extract a LoRA adapter by comparing two models: a censored Llama 3.1 (meta-llama/Meta-Llama-3-8B-Instruct) and an abliterated Llama 3.1 (mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated). Merge: We merge this new LoRA adapter using task arithmetic to the censored NousResearch/Hermes-3-Llama-3.1-8B to abliterate it.

hermes-3-llama-3.2-3b

Hermes 3 3B is a small but mighty new addition to the Hermes series of LLMs by Nous Research, and is Nous's first fine-tune in this parameter class. Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.

doctoraifinetune-3.1-8b-i1

This is a fine-tuned version of the Meta-Llama-3.1-8B-bnb-4bit model, specifically adapted for the medical field. It has been trained using a dataset that provides extensive information on diseases, symptoms, and treatments, making it ideal for AI-powered healthcare tools such as medical chatbots, virtual assistants, and diagnostic support systems. Key Features Disease Diagnosis: Accurately identifies diseases based on symptoms provided by the user. Symptom Analysis: Breaks down and interprets symptoms to provide a comprehensive medical overview. Treatment Recommendations: Suggests treatments and remedies according to medical conditions. Dataset The model is fine-tuned on 2000 rows from a dataset consisting of 272k rows. This dataset includes rich information about diseases, symptoms, and their corresponding treatments. The model is continuously being updated and will be further trained on the remaining data in future releases to improve accuracy and capabilities.

astral-fusion-neural-happy-l3.1-8b

Astral-Fusion-Neural-Happy-L3.1-8B is a celestial blend of magic, creativity, and dynamic storytelling. Designed to excel in instruction-following, immersive roleplaying, and magical narrative generation, this model is a fusion of the finest qualities from Astral-Fusion, NIHAPPY, and NeuralMahou. ✨🚀 This model is perfect for anyone seeking a cosmic narrative experience, with the ability to generate both precise instructional content and fantastical stories in one cohesive framework. Whether you're crafting immersive stories, creating AI roleplaying characters, or working on interactive storytelling, this model brings out the magic. 🌟

LocalAI model gallery list

🖼️ Available 599 models

Refer to the Model gallery for more information on how to use the models with LocalAI. You can install models with the CLI command local-ai models install . or by using the WebUI.

intellect-1-instruct

llama-3.3-70b-instruct

l3.3-70b-euryale-v2.3

l3.3-ms-evayale-70b

rwkv-6-world-7b

qwen2.5-coder-14b

qwen2.5-coder-3b-instruct

qwen2.5-coder-32b-instruct

qwen2.5-coder-14b-instruct

qwen2.5-coder-1.5b-instruct

qwen2.5-coder-7b-instruct

qwen2.5-coder-7b-3x-instruct-ties-v1.2-i1

qwen2.5-coder-7b-instruct-abliterated-i1

rombos-coder-v2.5-qwen-7b

rombos-coder-v2.5-qwen-32b

rombos-coder-v2.5-qwen-14b

qwen2.5-coder-32b-instruct-uncensored-i1

opencoder-8b-base

opencoder-8b-instruct

opencoder-1.5b-base

opencoder-1.5b-instruct

granite-3.0-1b-a400m-instruct

moe-girl-800ma-3bt

moe-girl-1ba-7bt-i1

salamandra-7b-instruct

llama-3.2-1b-instruct:q4_k_m

llama-3.2-3b-instruct:q4_k_m

llama-3.2-3b-instruct:q8_0

llama-3.2-1b-instruct:q8_0

versatillama-llama-3.2-3b-instruct-abliterated

llama3.2-3b-enigma

llama3.2-3b-esper2

llama-3.2-3b-agent007

llama-3.2-3b-agent007-coder

fireball-meta-llama-3.2-8b-instruct-agent-003-128k-code-dpo

llama-3.2-chibi-3b

llama-3.2-3b-reasoning-time

llama-3.2-sun-2.5b-chat

llama-3.2-3b-instruct-uncensored

calme-3.3-llamaloi-3b

calme-3.2-llamaloi-3b

calme-3.1-llamaloi-3b

llama3.2-3b-enigma

llama3.2-3b-shiningvaliant2-i1

llama-doctor-3.2-3b-instruct

onellm-doey-v1-llama-3.2-3b

llama-sentient-3.2-3b-instruct

llama-smoltalk-3.2-1b-instruct

qwen2.5-14b-instruct

qwen2.5-math-7b-instruct

qwen2.5-14b_uncencored

qwen2.5-coder-7b-instruct

qwen2.5-math-72b-instruct

qwen2.5-0.5b-instruct

qwen2.5-1.5b-instruct

qwen2.5-32b

qwen2.5-32b-instruct

qwen2.5-72b-instruct

bigqwen2.5-52b-instruct

replete-llm-v2.5-qwen-14b

replete-llm-v2.5-qwen-7b

calme-2.2-qwen2.5-72b-i1

t.e-8.1-iq-imatrix-request

rombos-llm-v2.5.1-qwen-3b

qwen2.5-7b-ins-v3

supernova-medius

eva-qwen2.5-14b-v0.1-i1

cursorcore-qw2.5-7b-i1

cursorcore-qw2.5-1.5b-lc-i1

edgerunner-command-nested-i1

tsunami-0.5x-7b-instruct-i1

qevacot-7b-v2

meissa-qwen2.5-7b-instruct

thebeagle-v2beta-32b-mgs

Refer to the Model gallery for more information on how to use the models with LocalAI.
You can install models with the CLI command `local-ai models install` . or by using the WebUI.