AI Models

Browse and discover AI models across various categories.

New!stable-diffusion-3-medium

stabilityai

Total Score

850

stable-diffusion-3-medium is a cutting-edge Multimodal Diffusion Transformer (MMDiT) text-to-image generative model developed by Stability AI. It features significant improvements in image quality, typography, complex prompt understanding, and resource-efficiency compared to earlier versions of Stable Diffusion. The model utilizes three fixed, pretrained text encoders - OpenCLIP-ViT/G, CLIP-ViT/L, and T5-xxl - to enable these enhanced capabilities. Model inputs and outputs stable-diffusion-3-medium is a text-to-image model, meaning it takes text prompts as input and generates corresponding images as output. The model can handle a wide range of text prompts, from simple descriptions to more complex, multi-faceted prompts. Inputs Text prompts describing the desired image Outputs Generated images that match the input text prompts Capabilities stable-diffusion-3-medium excels at generating high-quality, photorealistic images from text prompts. It demonstrates significant improvements in areas like image quality, typography, and the ability to understand and generate images for complex prompts. The model is also resource-efficient, making it a powerful tool for a variety of applications. What can I use it for? stable-diffusion-3-medium can be used for a wide range of creative and professional applications, such as generating images for art, design, advertising, and even film and video production. The model's capabilities make it well-suited for projects that require visually striking, high-quality images based on text descriptions. Things to try One interesting aspect of stable-diffusion-3-medium is its ability to generate images with a strong sense of typography and lettering. You can experiment with prompts that include specific font styles or text compositions to see how the model handles these more complex visual elements. Additionally, you can try combining stable-diffusion-3-medium with other Stable Diffusion models, such as stable-diffusion-img2img or stable-diffusion-inpainting, to explore even more creative possibilities.

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Updated 6/12/2024

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Qwen2-72B-Instruct

Qwen

Total Score

263

Qwen2-72B-Instruct is the 72 billion parameter version of the Qwen2 series of large language models developed by Qwen. Compared to the state-of-the-art open-source language models, including the previous Qwen1.5 release, Qwen2 has generally surpassed most open-source models and demonstrated competitiveness against proprietary models across a range of benchmarks targeting language understanding, generation, multilingual capability, coding, mathematics, and reasoning. The Qwen2-72B-Instruct model specifically has been instruction-tuned, enabling it to excel at a variety of tasks. The Qwen2 series, including the Qwen2-7B-Instruct and Qwen2-72B models, is based on the Transformer architecture with improvements like SwiGLU activation, attention QKV bias, and group query attention. Qwen has also developed an improved tokenizer that is adaptive to multiple natural languages and codes. Model inputs and outputs Inputs Text prompts for language generation, translation, summarization, and other language tasks Outputs Texts generated in response to the input prompts, with the model demonstrating strong performance on a variety of natural language processing tasks. Capabilities The Qwen2-72B-Instruct model has shown strong performance on a range of benchmarks, including language understanding, generation, multilingual capability, coding, mathematics, and reasoning. For example, it surpassed open-source models like LLaMA and Yi on the MMLU (Multimodal Language Understanding) benchmark, and outperformed them on coding tasks like HumanEval and MultiPL-E. The model also exhibited competitive performance against proprietary models like ChatGPT on Chinese language benchmarks like C-Eval. What can I use it for? The Qwen2-72B-Instruct model can be used for a variety of natural language processing tasks, including text generation, language translation, summarization, and question answering. Its strong performance on coding and mathematical reasoning benchmarks also makes it suitable for applications like code generation and problem-solving. Given its multilingual capabilities, the model can be leveraged for international and cross-cultural projects. Things to try One interesting aspect of the Qwen2-72B-Instruct model is its ability to handle long input texts. By utilizing the YARN technique for enhancing model length extrapolation, the model can process inputs up to 131,072 tokens, enabling the processing of extensive texts. This could be useful for applications that require working with large amounts of textual data, such as document summarization or question answering over lengthy passages.

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Updated 6/12/2024

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WizardLM-13B-V1.2

WizardLMTeam

Total Score

217

The WizardLM-13B-V1.2 model is a large pre-trained language model developed by the WizardLM team. It is a full-weight version of the WizardLM-13B model, which is based on the Llama-2 13b model. The WizardLM team has also released larger versions of the model, including the WizardLM-70B-V1.0 which slightly outperforms some closed-source LLMs on benchmarks. Model inputs and outputs The WizardLM-13B-V1.2 model is a text-to-text transformer that can be used for a variety of natural language processing tasks. It takes text prompts as input and generates relevant text responses. Inputs Text prompts or instructions for the model to follow Outputs Coherent, multi-sentence text responses that address the input prompts Capabilities The WizardLM-13B-V1.2 model is capable of following complex instructions and engaging in open-ended conversations. It has been shown to outperform other large language models on benchmarks like MT-Bench, AlpacaEval, and WizardEval. For example, the model achieves 36.6 pass@1 on the HumanEval benchmark, demonstrating its ability to generate solutions to complex coding problems. What can I use it for? The WizardLM-13B-V1.2 model could be useful for a wide range of applications that require natural language understanding and generation, such as: Engaging in open-ended conversations and answering questions Providing detailed and helpful responses to instructions and prompts Assisting with coding and software development tasks Generating human-like text for creative writing or content creation Things to try One interesting thing to try with the WizardLM-13B-V1.2 model is to provide it with complex, multi-step instructions and observe how it responds. The model's ability to follow intricate prompts and generate coherent, detailed responses is a key capability. You could also try using the model for tasks like code generation or mathematical reasoning, as the WizardLM team has shown the model's strong performance on benchmarks like HumanEval and GSM8k.

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Updated 6/12/2024

👨‍🏫

Qwen2-7B-Instruct

Qwen

Total Score

212

The Qwen2-7B-Instruct is the 7 billion parameter instruction-tuned language model from the Qwen2 series of large language models developed by Qwen. Compared to state-of-the-art open-source language models like LLaMA and ChatGLM, the Qwen2 series has generally surpassed them in performance across a range of benchmarks targeting language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning. The Qwen2 series includes models ranging from 0.5 to 72 billion parameters, with the Qwen2-7B-Instruct being one of the smaller yet capable instruction-tuned variants. It is based on the Transformer architecture with enhancements like SwiGLU activation, attention QKV bias, and group query attention. The model also uses an improved tokenizer that is adaptive to multiple natural languages and coding. Model inputs and outputs Inputs Text**: The model can take text inputs of up to 131,072 tokens, enabling processing of extensive inputs. Outputs Text**: The model generates text outputs, which can be used for a variety of natural language tasks such as question answering, summarization, and creative writing. Capabilities The Qwen2-7B-Instruct model has shown strong performance across a range of benchmarks, including language understanding (MMLU, C-Eval), mathematics (GSM8K, MATH), coding (HumanEval, MBPP), and reasoning (BBH). It has demonstrated competitiveness against proprietary models in these areas. What can I use it for? The Qwen2-7B-Instruct model can be used for a variety of natural language processing tasks, such as: Question answering**: The model can be used to answer questions on a wide range of topics, drawing upon its broad knowledge base. Summarization**: The model can be used to generate concise summaries of long-form text, such as articles or reports. Creative writing**: The model can be used to generate original text, such as stories, poems, or scripts, with its strong language generation capabilities. Coding assistance**: The model's coding knowledge can be leveraged to help with tasks like code generation, explanation, and debugging. Things to try One interesting aspect of the Qwen2-7B-Instruct model is its ability to process long-form text inputs, thanks to its large context length of up to 131,072 tokens. This can be particularly useful for tasks that require understanding and reasoning over extensive information, such as academic papers, legal documents, or historical archives. Another area to explore is the model's multilingual capabilities. As mentioned, the Qwen2 series, including the Qwen2-7B-Instruct, has been designed to be adaptive to multiple languages, which could make it a valuable tool for cross-lingual applications.

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Updated 6/12/2024

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Higgs-Llama-3-70B

bosonai

Total Score

139

Higgs-Llama-3-70B is a post-trained version of Meta-Llama/Meta-Llama-3-70B, specially tuned for role-playing while remaining competitive in general-domain instruction-following and reasoning. The model was developed by bosonai. Through supervised fine-tuning with instruction-following and chat datasets, as well as preference pair optimization, the model is designed to follow assigned roles more closely than other instruct models. Model inputs and outputs Inputs The model takes in text input only. Outputs The model generates text and code outputs. Capabilities Higgs-Llama-3-70B excels at role-playing tasks while maintaining strong performance on general language understanding and reasoning benchmarks. The model was evaluated on the MMLU-Pro and Arena-Hard benchmarks, where it achieved competitive results compared to other leading LLMs. What can I use it for? Higgs-Llama-3-70B is well-suited for applications that require natural language interaction and task completion, such as conversational AI assistants, content generation, and creative writing. The model's strong performance on role-playing tasks makes it particularly useful for dialogue-driven applications that involve characters or personas. Things to try Try prompting the model with different role-playing scenarios or instructions to see how it adapts its language and behavior to match the specified context. Additionally, you can explore the model's capabilities on open-ended language tasks by providing it with a variety of prompts and observing the quality and coherence of the generated outputs.

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Updated 6/12/2024

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WizardCoder-33B-V1.1

WizardLMTeam

Total Score

124

WizardCoder-33B-V1.1 is a large language model (LLM) developed by the WizardLM team that is trained to excel at code-related tasks. It is based on the DeepSeek-Coder-33B-base model and has been further fine-tuned using the Evol-Instruct method to improve its code generation and understanding capabilities. Compared to previous versions, WizardCoder-33B-V1.1 achieves state-of-the-art performance on several industry-standard benchmarks, outperforming models like ChatGPT 3.5, Gemini Pro, and DeepSeek-Coder-33B-instruct. Model inputs and outputs Inputs Natural language instructions**: The model accepts natural language descriptions of coding tasks or problems that it should solve. Outputs Generated code**: The model's primary output is Python, Java, or other programming language code that attempts to fulfill the given instruction or solve the provided problem. Capabilities WizardCoder-33B-V1.1 demonstrates impressive abilities in generating functional code to solve a wide variety of programming tasks. It achieves 79.9 pass@1 on the HumanEval benchmark, 73.2 pass@1 on HumanEval-Plus, 78.9 pass@1 on MBPP, and 66.9 pass@1 on MBPP-Plus. These results show the model's strong performance compared to other code LLMs, making it a valuable tool for developers and programmers. What can I use it for? The WizardCoder-33B-V1.1 model can be utilized in a range of applications that involve code generation or understanding, such as: Automated code completion and suggestions to assist developers Prototyping and building initial versions of software applications Translating natural language descriptions into working code Educational tools for teaching programming concepts and skills Augmenting human programming workflows to boost productivity Things to try One interesting aspect of WizardCoder-33B-V1.1 is its ability to handle complex, multi-part instructions and generate code that addresses all the requirements. You could try providing the model with detailed prompts involving various coding tasks and see how it responds. Additionally, experimenting with different decoding strategies, such as adjusting the temperature or number of samples, may uncover further nuances in the model's capabilities.

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Updated 6/12/2024

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WizardMath-70B-V1.0

WizardLMTeam

Total Score

116

The WizardMath-70B-V1.0 model is a large language model developed by the WizardLM team that is focused on empowering mathematical reasoning capabilities. It was trained using a novel method called Reinforced Evol-Instruct (RLEIF), which involves automatically generating a diverse set of math-related instructions to fine-tune the model. The model is one of several in the WizardMath series, which also includes smaller 13B and 7B versions. Compared to other open-source math LLMs, the WizardMath-70B-V1.0 model significantly outperforms on key benchmarks like the GSM8k and MATH datasets, achieving 81.6 pass@1 and 22.7 pass@1 respectively. This puts it ahead of state-of-the-art models like ChatGPT 3.5, Claude Instant 1, and PaLM 2 540B. Model inputs and outputs Inputs Natural language instructions**: The model takes in text-based instructions or prompts related to math problems or reasoning tasks. Outputs Textual responses**: The model generates text-based responses that attempt to solve the given math problem or provide a reasoned answer. Capabilities The WizardMath-70B-V1.0 model demonstrates strong capabilities in mathematical reasoning and problem-solving. It can tackle a wide range of math-related tasks, from simple arithmetic to more complex algebra, geometry, and even calculus problems. The model is particularly adept at step-by-step reasoning, clearly explaining its thought process and showing its work. What can I use it for? The WizardMath-70B-V1.0 model could be useful for a variety of applications that require advanced mathematical skills, such as: Providing homework help and tutoring for students struggling with math Automating the generation of math practice problems and solutions Integrating math reasoning capabilities into educational apps and games Aiding in the development of math-focused AI assistants Things to try One interesting aspect of the WizardMath-70B-V1.0 model is its ability to handle multi-step math problems. Try providing it with complex word problems or story-based math questions and see how it breaks down the problem and arrives at the solution. You can also experiment with prompting the model to explain its reasoning in more detail or to explore alternative solution approaches.

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Updated 6/12/2024

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glm-4v-9b

THUDM

Total Score

115

glm-4v-9b is a large language model developed by THUDM, a leading AI research group. It is part of the GLM (General Language Model) family, which aims to create open, bilingual language models capable of strong performance across a wide range of tasks. The glm-4v-9b model builds upon the successes of earlier GLM models, incorporating advanced techniques like autoregressive blank infilling and hybrid pretraining objectives. This allows it to achieve impressive results on benchmarks like MMBench-EN-Test, MMBench-CN-Test, and SEEDBench_IMG, outperforming models like GPT-4-turbo-2024-04-09, Gemini 1.0, and Qwen-VL-Max. Compared to similar large language models, glm-4v-9b stands out for its strong multilingual and multimodal capabilities. It can seamlessly handle both English and Chinese, and has been trained to integrate visual information with text, making it well-suited for tasks like image captioning and visual question answering. Model Inputs and Outputs Inputs Text**: The model can accept text input in the form of a conversation, with the user's message formatted as {"role": "user", "content": "query"}. Images**: Along with text, the model can also take image inputs, which are passed through the tokenizer using the image field in the input template. Outputs Text Response**: The model will generate a text response to the provided input, which can be retrieved by decoding the model's output tokens. Conversation History**: The model maintains a conversation history, which can be passed back into the model to continue the dialogue in a coherent manner. Capabilities The glm-4v-9b model has demonstrated strong performance on a wide range of benchmarks, particularly those testing multilingual and multimodal capabilities. For example, it achieves high scores on the MMBench-EN-Test (81.1), MMBench-CN-Test (79.4), and SEEDBench_IMG (76.8) tasks, showcasing its ability to understand and generate text in both English and Chinese, as well as integrate visual information. Additionally, the model has shown promising results on tasks like MMLU (58.7), AI2D (81.1), and OCRBench (786), indicating its potential for applications in areas like question answering, image understanding, and optical character recognition. What Can I Use It For? The glm-4v-9b model's strong multilingual and multimodal capabilities make it a versatile tool for a variety of applications. Some potential use cases include: Intelligent Assistants**: The model's ability to engage in natural language conversations, while also understanding and generating content related to images, makes it well-suited for building advanced virtual assistants that can handle a wide range of user requests. Multimodal Content Generation**: Leveraging the model's text-image integration capabilities, developers can create applications that generate multimedia content, such as image captions, visual narratives, or even animated stories. Multilingual Language Understanding**: Organizations operating in diverse language environments can use glm-4v-9b to build applications that can seamlessly handle both English and Chinese, enabling improved cross-cultural communication and collaboration. Research and Development**: As an open-source model, glm-4v-9b can be a valuable resource for AI researchers and developers looking to explore the latest advancements in large language models and multimodal learning. Things to Try One key feature of the glm-4v-9b model is its ability to effectively utilize both textual and visual information. Developers and researchers can experiment with incorporating image data into their applications, exploring how the model's multimodal capabilities can enhance tasks like image captioning, visual question answering, or even image-guided text generation. Another avenue to explore is the model's strong multilingual performance. Users can try interacting with the model in both English and Chinese, and observe how it maintains coherence and contextual understanding across languages. This can lead to insights on building truly global AI systems that can bridge language barriers. Finally, the model's impressive benchmark scores suggest that it could be a valuable starting point for fine-tuning or further pretraining on domain-specific datasets. Developers can experiment with adapting the model to their particular use cases, unlocking new capabilities and expanding the model's utility.

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Updated 6/12/2024

🤿

Qwen2-72B

Qwen

Total Score

104

The Qwen2-72B is a large-scale language model developed by Qwen, a team at Alibaba Cloud. It is part of the Qwen series of language models, which includes models ranging from 0.5 to 72 billion parameters. Compared to other open-source language models, Qwen2-72B has demonstrated strong performance across a variety of benchmarks targeting language understanding, generation, multilingual capability, coding, mathematics, and reasoning. The model is based on the Transformer architecture and includes features like SwiGLU activation, attention QKV bias, group query attention, and an improved tokenizer that is adaptive to multiple natural languages and codes. Qwen2-72B has a large vocabulary of over 150,000 tokens, which enables efficient encoding of Chinese, English, and code data, as well as strong support for a wide range of other languages. Similar to other models in the Qwen series, Qwen2-72B is a decoder-only language model that is not recommended for direct text generation. Instead, Qwen suggests applying techniques like supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), or continued pretraining to further enhance the model's capabilities. Model inputs and outputs Inputs The model takes in text input, which can be in a variety of languages including Chinese, English, and multilingual text. Outputs The model generates text output, which can be used for a variety of natural language processing tasks such as language understanding, generation, translation, and more. Capabilities Qwen2-72B has demonstrated strong performance on a wide range of benchmarks, including commonsense reasoning, mathematical reasoning, coding, and multilingual tasks. For example, on the MMLU (Multi-Model Language Understanding) benchmark, Qwen2-72B achieved an average score of 77.4%, outperforming other large language models like Qwen-72B and Qwen1.5-72B. The model also showed impressive performance on coding tasks like HumanEval and MBPP, as well as mathematical reasoning tasks like GSM8K and MATH. What can I use it for? The Qwen2-72B model can be used for a variety of natural language processing tasks, such as: Text generation**: While the model is not recommended for direct text generation, it can be fine-tuned or used as a base for developing more specialized language models for tasks like content creation, dialogue systems, or summarization. Language understanding**: The model's strong performance on benchmarks like MMLU suggests it can be useful for tasks like question answering, textual entailment, and other language understanding applications. Multilingual applications**: The model's broad vocabulary and support for multiple languages make it well-suited for developing multilingual applications, such as translation systems or cross-lingual information retrieval. Code-related tasks**: Given the model's strong performance on coding-related benchmarks, it could be leveraged for tasks like code generation, code summarization, or code understanding. Things to try One interesting aspect of the Qwen2-72B model is its ability to handle long-context input. The model supports a context length of up to 32,768 tokens, which is significantly longer than many other language models. This makes it well-suited for tasks that require understanding and reasoning over long passages of text, such as summarization, question answering, or document-level language modeling. Another interesting area to explore would be the model's performance on specialized domains or tasks, such as scientific or technical writing, legal reasoning, or financial analysis. By fine-tuning the model on domain-specific data, researchers and developers may be able to unlock additional capabilities and insights.

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Updated 6/12/2024

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L3-8B-Stheno-v3.2

Sao10K

Total Score

79

The L3-8B-Stheno-v3.2 is an experimental AI model created by Sao10K that is designed for immersive roleplaying and creative writing tasks. It builds upon previous versions of the Stheno model, with updates to the training data, hyperparameters, and overall performance. Compared to the similar L3-8B-Stheno-v3.1 model, v3.2 incorporates a mix of SFW and NSFW writing samples, more instruction/assistant-style data, and improved coherency and prompt adherence. The L3-8B-Stheno-v3.1-GGUF-IQ-Imatrix variant also offers quantized versions for lower VRAM requirements. Another related model, the Fimbulvetr-11B-v2 from Sao10K, is a solar-based model focused on high-quality 3D renders and visual art generation. Model inputs and outputs The L3-8B-Stheno-v3.2 model is a text-to-text generation model designed for interactive roleplaying and creative writing tasks. It takes in prompts, system instructions, and user inputs, and generates relevant responses and story continuations. Inputs Prompts**: Short text descriptions or instructions that set the context for the model's response System instructions**: Guidelines for the model's persona and expected behavior, such as roleplaying a specific character User inputs**: Conversational messages or story continuations provided by the human user Outputs Narrative responses**: Creative, coherent text continuations that advance the story or conversation Character dialogue**: Believable, in-character responses that maintain the model's persona Descriptive details**: Vivid, immersive descriptions of scenes, characters, and actions Capabilities The L3-8B-Stheno-v3.2 model excels at open-ended roleplaying and storytelling tasks. It is capable of handling a wide range of scenarios, from fantastical adventures to intimate character interactions. The model maintains a strong sense of character and can fluidly continue a narrative, adapting to the user's prompts and inputs. Compared to earlier versions, v3.2 demonstrates improved handling of NSFW content, better assistant-style task performance, and enhanced multi-turn coherency. The model is also more adept at following prompts and instructions while still retaining its creative flair. What can I use it for? The L3-8B-Stheno-v3.2 model is well-suited for a variety of interactive, text-based experiences. Some potential use cases include: Roleplaying games**: The model can serve as an interactive roleplaying partner, responding to user prompts and advancing the story in real-time. Creative writing collaborations**: Users can work with the model to co-create engaging narratives, with the model generating compelling continuations and descriptive details. Conversational AI assistants**: The model's ability to maintain character and engage in natural dialogue makes it a potential candidate for more advanced AI assistants. Things to try One interesting aspect of the L3-8B-Stheno-v3.2 model is its ability to handle a mix of SFW and NSFW content. Users can experiment with prompts that explore the model's range, testing its capabilities in both tasteful, family-friendly scenarios as well as more mature, adult-oriented situations. Another avenue to explore is the model's performance on assistant-style tasks, such as answering questions, providing explanations, or offering advice. Users can try crafting prompts that challenge the model to demonstrate its knowledge and problem-solving skills in a more practical, non-fiction oriented context. Overall, the L3-8B-Stheno-v3.2 model offers a versatile and engaging platform for immersive text-based experiences. Its combination of creative storytelling and adaptable conversational abilities make it a promising tool for a variety of applications.

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Updated 6/12/2024

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New!LLaMA-3-8B-SFR-Iterative-DPO-R

Salesforce

Total Score

70

LLaMA-3-8B-SFR-Iterative-DPO-R is a state-of-the-art instruct model developed by Salesforce. It outperforms similar-sized models, most large open-sourced models, and strong proprietary models on three widely-used instruct model benchmarks: Alpaca-Eval-V2, MT-Bench, and Chat-Arena-Hard. The model is trained on open-sourced datasets without additional human or GPT4 labeling. The SFR-Iterative-DPO-LLaMA-3-8B-R model follows a similar approach, also outperforming other models on these benchmarks. Salesforce has developed an efficient online RLHF recipe for LLM instruct training, using a DPO-based method that is cheaper and simpler to train than PPO-based approaches. Model Inputs and Outputs Inputs Text prompts Outputs Generated text responses Capabilities The LLaMA-3-8B-SFR-Iterative-DPO-R model has shown strong performance on a variety of instruct model benchmarks. It can engage in open-ended conversations, answer questions, and complete tasks across a wide range of domains. What Can I Use It For? The LLaMA-3-8B-SFR-Iterative-DPO-R model can be used for building conversational AI assistants, automating text-based workflows, and generating content. Potential use cases include customer service, technical support, content creation, and task completion. As with any large language model, developers should carefully consider safety and ethical implications when deploying the model. Things to Try Try prompting the model with specific tasks or open-ended questions to see its versatility and capabilities. You can also experiment with different generation parameters, such as temperature and top-p, to control the model's output. Additionally, consider fine-tuning the model on your own data to adapt it to your specific use case.

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Updated 6/12/2024

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New!Qwen2-7B-Instruct-GGUF

Qwen

Total Score

60

The Qwen2-7B-Instruct-GGUF is a large language model in the Qwen2 series created by Qwen. Compared to the state-of-the-art open-source language models, including the previous Qwen1.5 release, Qwen2 has generally surpassed most open-source models and demonstrated competitiveness against proprietary models across a variety of benchmarks targeting language understanding, generation, multilingual capability, coding, mathematics, and reasoning. The Qwen2-7B-Instruct is a 7 billion parameter instruction-tuned version of the Qwen2 model, while the Qwen2-72B-Instruct is a larger 72 billion parameter version. The base Qwen2-7B and Qwen2-72B models are also available. Model inputs and outputs Inputs Text prompts**: The model can accept text prompts of up to 131,072 tokens for processing. This enables handling of extensive inputs. Outputs Text completions**: The model can generate coherent text completions in response to the input prompts. Capabilities The Qwen2-7B-Instruct-GGUF model has demonstrated strong performance on a variety of benchmarks, including language understanding tasks like MMLU and GPQA, coding tasks like HumanEval and MultiPL-E, and mathematics tasks like GSM8K and MATH. It has also shown impressive multilingual capabilities on datasets like C-Eval and AlignBench. What can I use it for? The Qwen2-7B-Instruct-GGUF model can be used for a wide range of natural language processing tasks, including text generation, question answering, language understanding, and even coding and mathematics problem-solving. Potential use cases include chatbots, content creation, academic research, and task automation. Things to try Given the model's strong performance on long-form text processing, one interesting thing to try would be generating high-quality, coherent responses to lengthy prompts or documents. The model's multilingual capabilities could also be explored by testing it on tasks involving multiple languages. Additionally, the base Qwen2 models could be fine-tuned for specific domains or applications to further enhance their capabilities.

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Updated 6/12/2024

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