30 Best Open Source LLMs (That You Can Use Online)
Want to test out the latest, hottest Open Source LLMs from trending AI companies such as Mistral AI? This article list the top 30 options and where to try them out without downloading them locally!
In the ever-evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as pivotal tools reshaping how we interact with technology. From simplifying complex data analysis to enabling sophisticated conversational agents, LLMs' capabilities are vast and varied. This article delves into some of the most promising open-source LLMs available online, providing insights into their strengths and benchmark performances.
What Are Large Language Models?
At the heart of recent advancements in AI lies Large Language Models – sophisticated algorithms trained on vast datasets to understand, interpret, and generate human language. Unlike their predecessors, these models grasp the nuances of language, context, and even sentiment, making them exceptionally versatile in tasks ranging from automated text generation to complex problem-solving.
Are LLMs really that large? The size of these models, often indicated by the number of parameters they contain, directly correlates to their ability to process and generate more nuanced and accurate responses. This capability has been a game-changer in natural language processing (NLP), enabling more human-like interactions between machines and users.
The Rise of Open Source in AI
The open-source movement has been instrumental in democratizing AI technology. By making LLMs available to a wider audience, open-source projects encourage innovation, collaboration, and rapid development. This open approach not only accelerates the advancement of AI technologies but also fosters a community where developers and researchers worldwide contribute to and benefit from shared knowledge and resources.
Benefits of Open Source LLMs
Accessibility: Open-source LLMs are more accessible to developers, researchers, and even hobbyists. This accessibility fuels a diverse range of applications and rapid experimentation.
Transparency: The open nature allows for greater transparency in how these models are built and trained, which is crucial for understanding and improving their performance.
Community Development: An open-source model grows with its community. Contributions from various sectors lead to more robust, versatile, and ethical AI models.
Cost-Effectiveness: Open-source models often provide a cost-effective alternative to proprietary models, especially for individuals and smaller organizations.
30+ Open Source LLMs that You Can Use Online
1. Mixtral 8x7B Instruct
Description: Mixtral 8x7B is a state-of-the-art large language model (LLM) developed by Mistral AI, featuring a Sparse Mixture of Experts architecture. This model is designed to follow instructions, complete requests, and generate creative text formats. It has been fine-tuned to be a helpful assistant, and it outperforms Llama 2 70B on most benchmarks. The model is known for its ability to be easily fine-tuned to achieve compelling performance, making it a versatile and powerful tool for various natural language processing tasks.
Strengths: The Mixtral 8x7B model demonstrates several strengths, including its ability to follow instructions, complete requests, and generate creative text formats. It has been optimized to be a helpful assistant, and its Sparse Mixture of Experts architecture contributes to its high performance across different benchmarks. The model's versatility and ease of fine-tuning make it a valuable resource for tasks that require accurate and creative language generation.
Benchmark: OThe Mixtral 8x7B model has been evaluated across various benchmarks, showcasing its performance in following instructions, completing requests, and generating creative text formats. It has been recognized for its compelling performance and its ability to outperform other models on most benchmarks. The model's release blog post provides full details of its performance and capabilities, positioning it as a leading LLM in the industry.
Description: The Dolphin 2.5 Mixtral 8x7B is an advanced large language model (LLM) developed by Eric Hartford, based on the Mixtral 8x7B architecture. This model is known for its proficiency in coding and its uncensored nature. It has been fine-tuned to be a helpful assistant and is designed to follow instructions, complete requests, and generate creative text formats. The model's training was sponsored by Convai, and it is based on the Mixtral 8x7B, with the base model having 32k context, fine-tuned with 16k. The Dolphin 2.5 Mixtral 8x7B is recognized for its obedience and proficiency in coding, making it a valuable resource for various natural language processing tasks.
Strengths: The Dolphin 2.5 Mixtral 8x7B model demonstrates several strengths, including its proficiency in coding, obedience, and its uncensored nature. These characteristics make it a reliable choice for tasks that require accurate and detailed language generation. The model's fine-tuning and training process have contributed to its high-quality performance across different natural language processing benchmarks.
Benchmark: The performance of the Dolphin 2.5 Mixtral 8x7B has been evaluated across various benchmarks, showcasing its capabilities in following instructions, completing requests, and generating creative text formats. The model has been recognized for its compelling performance and its ability to outperform other models on specific benchmarks. Its uncensored nature and proficiency in coding set it apart as a significant advancement in the field of AI language models, offering a new standard for various natural language processing applications.
Description: The Mistral-Medium is a large language model (LLM) developed by Mistral AI, featuring a sparse mixture of experts architecture with 12 billion active parameters. It supports a context window of 32,000 tokens (around 24,000 words) and is known for its proficiency in reasoning, code, JSON, and chat applications. The model is considered the flagship model of Mistral AI and is closed-source.
Strengths: The model's strengths include its proficiency in reasoning, code, JSON, and chat applications, as well as its support for a large context window and its closed-source, flagship status. However, there are also comments suggesting that its performance may not be as strong in comparison to other models on certain benchmarks.
Benchmark: It excels at reasoning and is priced at 2.5€ per 1 million tokens for input and 7.5€ per 1 million tokens for output. The Mistral-Medium has been evaluated on various benchmarks, with some feedback indicating that its performance is underwhelming in certain areas, such as the MMLU benchmark.
Description: Part of the Mistral AI's lineup, Mistral-Small is geared towards handling multiple languages and coding tasks. It represents a balance between performance and versatility.
Strengths: It supports multiple languages including English, French, Italian, German, and Spanish, alongside coding capabilities. This makes it a versatile choice for multilingual and technical applications.
Benchmark: On MT-Bench, Mistral-Small scores 8.3, demonstrating its capability in various languages and coding.
Description: Mistral-Tiny, an endpoint of Mistral AI, serves the Mistral 7B Instruct v0.2. It's designed for cost-effective performance, primarily focusing on English language processing.
Strengths: Its main strength lies in its cost-effectiveness, making it suitable for applications that require efficient and budget-friendly language processing solutions.
Benchmark: Mistral-Tiny achieves a score of 7.6 on MT-Bench, highlighting its proficiency in handling language tasks in English.
Description: OpenHermes-2.5 is a sophisticated language model designed to excel in understanding, generating, and interacting with human language. This model has been trained on a rich dataset that includes a significant portion of GPT-4 generated content. It uses formats like ChatML and GGUF, which contribute to its efficiency in processing and presenting information. OpenHermes-2.5 is notable for its performance improvements across multiple benchmarks and its ability to handle a wide range of tasks including roleplaying and task execution.
Strengths: OpenHermes-2.5 stands out for its creative and engaging writing, with some inconsistencies in instruction adherence and character consistency. It excels in real-time interactions and demonstrates proficiency in a variety of language tasks. The model has shown remarkable improvements over its predecessor, particularly in benchmarks like TruthfulQA and AGIEval, and in comparison with models like Orca or Llama-2 13B.
Benchmark Results: In various benchmarks, OpenHermes-2.5 has shown impressive results. For instance, in the GPT-4All benchmark set, it achieved an average score of 73.12, and in the AGI-Eval benchmark, it scored an average of 43.07%. In the BigBench Reasoning Test, its average score was 40.96%, and in the TruthfulQA test, it scored 53.04% on average. These results indicate its strong performance in diverse AI challenges, surpassing many of its counterparts.
Technical Specifications: OpenHermes-2.5's training involved a mix of GPT-4 generated content and high-quality AI datasets, including a significant portion dedicated to code instructions. The data went through a rigorous filtering process and was converted to ShareGPT and ChatML formats for optimal training efficiency.
Description: Mistral 7B Instruct is a language model designed to effectively follow instructions, complete requests, and generate creative text formats. This model is an improved instruct fine-tuned version of its predecessor, Mistral 7B Instruct v0.1.
Strengths: Mistral 7B Instruct excels in processing and generating responses based on specific instructions, which is particularly useful in tasks that require a high level of precision and adherence to guidelines. It's designed to be more aligned with user inputs, improving the relevance and accuracy of its outputs.
Benchmark and Performance: While being only a 7B model, its instructional tuning and benchmarks suggests enhanced performance in tasks requiring adherence to specific user queries and instructions.
Description: The Psyfighter v2 13B is a versatile language model developed to enhance prose and logic capabilities over its predecessor, the Tiefighter 13B. It incorporates CatV1 and LimaRP, excelling in story mode and providing detailed and high-quality outputs. The model is recommended for chat mode and Alpaca, as it is a merge of various other models, and it contains a LoRA trained on KoboldAI's Skein adventure dataset. The Psyfighter v2 13B is known for its improvisation skills and is particularly suitable for generating conversations and adventures.
Strengths: The model's strengths lie in its ability to provide long, detailed responses and its improved prose and logic capabilities. It is designed to offer high-quality outputs, especially in story mode, making it a valuable tool for various natural language processing tasks.
Benchmark: The performance of the Psyfighter v2 13B has been evaluated across different benchmarks, demonstrating its capabilities in generating human-like text and understanding user commands. It has been recognized for its advancements in AI architecture for sequence modeling tasks and has outperformed other models on specific benchmarks.
Description: The Code Llama 34B is a large language model designed for general code synthesis and understanding, specifically for Python. It is part of the Code Llama collection, which includes models ranging from 7 billion to 34 billion parameters. The model is an auto-regressive language model that uses an optimized architecture. It has been fine-tuned for instruction following and safer deployment.
Strengths: The strength of the Code Llama 34B model lies in its ability to generate text for code synthesis and understanding, particularly in the context of Python. As for benchmarks, the model has been evaluated on various metrics, including code synthesis and understanding, where it has shown competitive performance compared to other models, especially in the context of Python-related tasks.
Benchmark: The Code Llama 34B model has been evaluated using the HumanEval metric, which consists of 164 handcrafted programming problems aimed at testing the model's capabilities. According to the HumanEval benchmark tests, the Python-specialized 34B model scored 53.7 percent, the highest among the weights-available models. The standard 34B model scored 48.8 percent. These scores indicate the model's competitive performance in code synthesis and understanding, particularly in the context of Python-related tasks.
Description: The 34B LLM, also known as Code Llama, is an AI language model developed by Meta. It is designed to assist with coding tasks, such as code completion and debugging, and supports various programming languages like Python, C++, Java, and others. The model is trained with 500B tokens of code and code-related data, and it comes in three sizes: 7B, 13B, and 34B, each addressing different serving and latency requirements. The 34B model returns the best results and allows for better coding assistance, while the smaller 7B and 13B models are faster and more suitable for tasks requiring low latency, like real-time code completion.
Strengths: In terms of strengths, the 34B LLM has demonstrated high performance in coding assistance, with the 34B model returning the best results. It is also cost-effective, as the Llama-2 model is the fastest and most efficient of the two, costing approximately 30 times less per usage than GPT-4. Being open source, it can be modified and self-hosted, ensuring better data privacy and security compared to closed models.
Benchmark: In benchmarks, the 34B LLM has shown competitive performance. For example, on the MMLU (massive multitask language understanding) benchmark, the 34B LLM outperformed larger LLMs, including Llama 2-70B and Falcon-180B, with a score of 76.3 compared to Llama 2's score of 68.9.
Description: The Goliath 120-B is a large language model (LLM) known for its exceptional performance in various natural language processing tasks. It is highly regarded for its translation capabilities, cross-language understanding, instruction following, reading between the lines, handling complex scenarios, and humor comprehension. Users have reported that even the "normal" version of Goliath 120-B is great at roleplaying due to its deep understanding and instruction following abilities
Strengths: In terms of strength, the Goliath 120-B excels in roleplaying and is considered the best LLM for this purpose. It outperforms smaller models in prose, understanding, and handling complex scenarios. Its strength also lies in its ability to follow instructions, read between the lines, and comprehend humor, which are challenging tasks for smaller models.
Benchmark: While specific benchmark scores are not provided in the search results, the Goliath 120-B is highly praised for its performance in roleplaying and understanding complex scenarios, indicating its superiority in these aspects compared to other LLMs.
Description: The PPLX-70B is a large language model (LLM) developed by Perplexity AI, designed to process and generate human-like text based on the input it receives. It is an advanced AI model with 70 billion parameters, enabling it to understand and respond to a wide range of prompts and queries. The model is part of the PPLX series, which includes various LLMs tailored for different use cases and performance requirements.
Strengths: The PPLX-70B demonstrates enhanced language processing capabilities, allowing it to generate detailed and comprehensive responses across various domains. It showcases impressive performance in theoretical physics and offers concise and informative answers. Additionally, the model leverages the most up-to-date information from the internet, addressing limitations related to sharing current and factual responses. The PPLX-70B is also accessible via Perplexity Labs, providing a first-of-its-kind API for interacting with the model.
Benchmark: The performance of LLMs like the PPLX-70B is evaluated using standardized benchmarks that measure their capabilities across diverse tasks and domains. Some commonly referenced LLM benchmarks include BIG (Beyond the Imitation Game), which covers over 200 tasks across 10 categories, MBPP (Python programming problems) for assessing code generation abilities, and MMLU (5-shot) for language understanding tasks. These benchmarks provide a holistic view of the model's performance and are essential for understanding its strengths and weaknesses, ultimately aiding in the selection of the most suitable LLM for specific applications.
Description: The PPLX-7B is a new online large language model (LLM) developed by Perplexity AI, featuring 7 billion parameters. It is designed to provide up-to-date, factual, and helpful responses by leveraging the most current information from the internet. The model is accessible via the PPLX API, making it the first of its kind to offer this capability. The PPLX-7B is part of Perplexity's efforts to address the limitations of existing LLMs related to sharing current and accurate information, known as "freshness," and avoiding inaccurate statements, known as "hallucinations".
Strengths: The PPLX-7B demonstrates impressive processing speed and efficiency, generating answers within seconds and producing an average of 140 tokens per second. It is particularly adept at providing comprehensive and detailed information, making it a valuable resource for various domains, including theoretical physics. The model's focus on delivering up-to-date and factual responses sets it apart from other LLMs, addressing the challenge of maintaining the freshness of information.
Benchmark: The PPLX-7B's performance has been evaluated in comparison to other LLMs, demonstrating its ability to match and even outperform existing models such as GPT-3.5 and llama2-70b, particularly in providing accurate and up-to-date responses. The model's emphasis on overcoming the limitations of existing LLMs positions it as a significant advancement in the field of AI language models, offering a new standard for addressing the challenges of information retrieval and accuracy.
Description: The Nous Hermes 70B, also known as Nous-Hermes-Llama2-70b, is a state-of-the-art language model developed by Nous Research. It has been fine-tuned on over 300,000 instructions, making it a powerful tool for a wide range of natural language processing tasks. The model is designed to provide long responses, lower hallucination rates, and does not incorporate OpenAI censorship mechanisms in the synthetic training data. It stands out for its ability to generate detailed and accurate content, making it suitable for various language-related applications.
Strengths: The Nous Hermes 70B model demonstrates several strengths, including the ability to provide long and detailed responses, lower hallucination rates, and the absence of OpenAI censorship mechanisms in its training data. These characteristics make it a reliable choice for tasks that require accurate and in-depth language generation. The model's fine-tuning process, led by Nous Research, Teknium, and Emozilla, has contributed to its high-quality performance across different natural language processing benchmarks.
Benchmark: The model has been evaluated across various benchmarks, showcasing its performance on tasks such as ARC (a question-answering benchmark), BoolQ (a natural language inference dataset), Hellaswag (a commonsense reasoning benchmark), OpenBookQA (a multiple-choice question-answering benchmark), PIQA (a physical commonsense reasoning benchmark), and Winogrande (a commonsense reasoning benchmark). The model's performance on these benchmarks reflects its strong capabilities in understanding and generating human-like text across different domains.
Description: Airoboros L 2 70B is an advanced AI model designed for a range of complex tasks, including coding, agent/function calling, and chain-of-thought processing. It leverages a sophisticated approach for generating responses based on input criteria, supporting both JSON and YAML outputs. The model is particularly adept at handling detailed coding instructions and complex query responses, and it includes capabilities for execution planning and handling multi-step instructions.
Strengths: One of the key strengths of Airoboros L-2 70-b is its ability to handle complex coding tasks with specific criteria. It can create applications based on detailed requirements, write multi-threaded servers, and even generate optimal responses to instructions utilizing a set of provided tools. The model supports a variety of formats for these tasks, including Python and C programming languages. Additionally, its chain-of-thought feature allows it to provide several possible responses to a given problem, rank them according to logical reasoning, and select the most feasible one.
Usage: To get the best results from Airoboros L-2 70-b, it's recommended to structure prompts with specific start and end instruction markers. This helps the model to better understand and respond to the instructions. It can handle a range of complex queries and tasks, making it a versatile tool for various applications.
Description: The Synthia-70B is a large language model (LLM) developed by Migel Tissera. It is a LLama-2-70B model trained on Orca style datasets and has been fine-tuned for instruction following as well as having long-form conversations. This model has been recognized for its blend of intelligence, personality, and humor.
Benchmarks: It has achieved a score of over 70 in the Open LLM Leaderboard, matching or exceeding the performance of ChatGPT-3.5. The Synthia-70B has been evaluated on a wide range of tasks, and it has shown strong performance on metrics such as arc_challenge, hellaswag, and mmlu.
Strength: In terms of strength, the model is known for providing correct answers to a high percentage of multiple-choice questions and for its ability to follow instructions and acknowledge all data input.
Requirements: The hardware requirements for running the Synthia-70B model smoothly include a reasonably modern consumer-level CPU with decent core count and clocks, along with a high-end GPU for best performance. The model is available in various file formats like GGUF, GPTQ, and HF. The Synthia-70B has been recognized as a strong performer in comparison tests, and it has been recommended for serious use due to its ability to provide the most correct answers
Description: The Mythalion-13B is a large language model (LLM) that has been developed by PygmalionAI. It is a merge of Pygmalion-2 13B and MythoMax 13B models. This model has been optimized for speed and context length, with a context of 8192 and 17.4M tokens
Strength: The model is available in various quantization methods, each with different trade-offs between model size and quality. For example, the mythalion-13b.Q5_K_S.gguf model has a size of 9.0 GB and is recommended for its relatively low quality loss, while the mythalion-13b.Q8_0.gguf model has a size of 13.8 GB and is not recommended for most purposes due to its extremely low quality loss
Benchamrks: In terms of benchmarks, the model has been reported to have a score of 49. The Mythalion-13B has been noted to perform well in providing correct answers, and in a comparison test, it was found to have given the most correct answers among the models tested, even outperforming some 70B models
Description: Yi 34-b is a large bilingual (English/Chinese) language model developed by the Chinese AI startup 01.AI. It contains 34 billion parameters and has been trained on a dataset that supports a 4K sequence length, which can be expanded to 32K during inference. This model has been fine-tuned for various chat use cases and can handle an impressive context window of up to 200K.
Notable Features: Yi 34-b has achieved state-of-the-art performance in areas like reading comprehension, common sense reasoning, and math/coding tasks. This performance, coupled with its bilingual capabilities, positions it as a significant competitor in the realm of open-source large language models.
Applications: Given its large parameter count and bilingual nature, Yi 34-b is suited for a variety of NLP tasks, including text classification, sentiment analysis, and question answering.
Access and Usage: Yi 34-b can be deployed locally, provided sufficient hardware resources are available. It is also accessible through APIs, with options for users who prefer not to deploy locally. This model is available for both academic and commercial use under certain conditions specified in the Model License Agreement 2.0.
Background: The creation of Yi 34-b is part of 01.AI's efforts to develop AI systems tailored for the Chinese market. This effort is in response to limited access to systems from other global AI organizations and is led by Kai-Fu Lee, a renowned computer scientist. Yi 34-b's development has contributed to 01.AI achieving unicorn status, with a valuation of over $1 billion.
Description: Yi 6-b appears to be a more compact version of the Yi series, designed for efficient language processing with a focus on specific use cases.
Strengths: This model might excel in applications where quick and accurate language processing is required but with limited computational resources.
Benchmark: Performance metrics could include response time, accuracy in understanding and responding to queries, and overall efficiency in different scenarios.
Description: The Noromaid-20B is a large language model (LLM) developed by IkariDev and Undi. It is available in various versions, such as v0.1.1, and is known for its lively and coherent character responses, making it suitable for roleplay and storytelling. The model is offered in different formats, such as GGUF, with varying quality and size specifications, allowing users to choose the most suitable version for their specific use case.
Strengths: One of the strengths of Noromaid-20B is its performance in creating lewd stories and roleplay scenarios. Users have reported that it outperforms its 13B variant by a significant degree, particularly in generating lively and less algorithmic/predictable character responses.
Benchmark: In terms of benchmarks, Noromaid-20B has been compared to other models such as Mythomax 13B, with users noting better results and more lively character responses compared to the alternative model. The model is also available on platforms like Anakin AI, where it is described as being on the pricier side but delivering better results in initial tests.
Description: The Llama 2-70B Instruct v2 is a language model developed by Upstage. It is available in GGML format and is part of the Open LLM Leaderboard. The model is generously supported by a grant from Andreessen Horowitz (a16z).
Strengths: The model runs on Nvidia A100 (80GB) GPU hardware, and predictions typically complete within 39 seconds. As for the benchmarks, it is important to note that the model was evaluated using MT-bench, a set of challenging multi-turn open-ended questions. This demonstrates the model's ability to handle complex language understanding and generation tasks.
Benchmark: The model's strength lies in its performance on various benchmarks. For instance, on the Open LLM Leaderboard, it achieved a score of 72.3 on the H4 benchmark, 70.9 on ARC, 87.5 on HellaSwag, 69.8 on MMLU, 61 on TruthfulQA, and 7.24375 on MT_Bench.
Description: The Llama-2-13B is a state-of-the-art language model developed by Meta, boasting 13 billion parameters. It is part of the Llama 2 family and is designed for a wide range of natural language processing (NLP) tasks.
Strengths: The model's strength lies in its ability to handle a high number of requests per second, minimize latency, and provide cost-effective solutions for NLP tasks.
Benchmark: The Llama-2-13B model has been benchmarked for performance in terms of latency, requests per second, and cost, which are crucial for evaluating its suitability based on business requirements. The model has shown superiority in performance metrics and has been fine-tuned to specialize in specific tasks, making it a versatile and powerful tool for various NLP applications.
Description: PaLM 2 is Google's next-generation large language model, designed to excel at tasks like advanced reasoning, translation, and code generation. It is built on Google's legacy of breakthrough research in machine learning and responsible AI.
Strengths: The model architecture and objective have also been updated to achieve overall better performance, faster inference, fewer parameters to serve, and a lower serving cost.
Benchmark: PaLM 2 improves upon its predecessor, PaLM, by unifying three distinct research advancements in large language models. It has an improved dataset mixture, including hundreds of human and programming languages, mathematical equations, scientific papers, and web pages.
Description: The Mistral-7B-OpenOrca is a large language model (LLM) released by MistralAI. It is the first 7B model to score better overall than all other models below 30B, achieving 98% of Llama2-70B-chat's performance.
Strengths: The model's performance has been compared to other LLMs, such as Llama2-70B, and has been shown to achieve competitive results. The Mistral-7B-OpenOrca has also been ranked #1 for all models smaller than 30B at the time of its release, further highlighting its high performance.
Benchmark: The model is known for its class-breaking performance and is capable of running fully accelerated on even moderate consumer GPUs. It is an open model, allowing for broader accessibility and use. The Mistral-7B-OpenOrca has been evaluated using various benchmark tests, including BigBench, GPT4ALL Leaderboard, MT-Bench, and AGIEval. It has been found to outperform the base Mistral-7B model and other 7B and 13B models in terms of performance.
Description: Neural Chat 7B is a LLM model focused on creating engaging and dynamic conversational experiences, using advanced neural network technologies for a variety of applications.
Strengths: Its potential strengths could include its ability to conduct natural, flowing conversations, making it suitable for chatbots and virtual assistant applications.
Benchmark: Performance evaluation would likely focus on its conversational abilities, including language understanding, response relevance, and engagement.
Description: The MythoMist 7B is a highly experimental Mistral-based merge model, actively benchmarked during its development. It comes in various quantization formats, each with different trade-offs between model size and quality. The model is created by Gryphe Padar and is generously supported by a grant from andreessen horowitz (a16z).
Strengths: . The model's strength lies in its experimental nature and the active benchmarking process, which allows it to be tailored to specific user goals. The benchmarks provide insights into its performance across different use cases and contexts, helping users make informed decisions about its suitability for their specific applications.
Benchmark: Evalu. In benchmarks, it is compared with many flagship models of other companies, and its performance is evaluated in various contexts, such as coding, story generation, and other tasks.
Description: The OpenChat language model is an open-source large language model (LLM) that has gained attention for its impressive performance. It has been described as a "proof of concept" and one of the strongest 7B models available, with a unique training strategy called C-RLFT. The model has been fine-tuned with C-RLFT and has achieved the highest average performance among all 13B open models on three standard benchmarks.
Strengths: The OpenChat model has been praised for its performance, but there are also discussions about the limitations and challenges in evaluating and comparing different language models, including OpenChat.
Benchmark: OpenChat-3.5-1210 has claimed a 15 points improvement in HumanEval, surpassing GPT-4 in March. The model's performance has been validated using AGIEval, where only openchat-13b surpassed the base model.
Description: Zephyr-7B is a language model developed by Hugging Face, designed to be more than just a chatbot. It is a 7B parameter GPT-like model primarily designed for English, operating under a CC BY-NC 4.0 license. Unlike many other models, Zephyr-7B has been trained on a mix of public and synthetic datasets, contributing to its robustness and versatility.
Strengths: Zephyr-7B excels in performance and efficiency, achieving scores that closely correlate with human ratings of model outputs. It has been shown to outperform larger models on benchmarks like MT-Bench and AlpacaEval, which assess a model's performance and alignment method. Zephyr-7B's unique training methodology, Direct Preference Optimization (DPO), has given it an edge in performance and made it more helpful than other models.
Benchmark: At the time of its release, Zephyr-7B was the highest ranked 7B chat model on the MT-Bench and AlpacaEval benchmarks, with a score of 7.34 on AlpacaEval, indicating its superior performance compared to other models. However, it may have relative weaknesses in solving math problems, and further research is needed to close this gap.
Description: The Nous Capybara 34B is an open-source large language model (LLM) developed by NousResearch. It is known for its speed and efficiency, making it a valuable tool for various natural language processing tasks. The model is available in different versions, such as Limarpv3 and AWQ, tailored for specific applications and languages, including German.
Strengths: One of the key strengths of the Nous Capybara 34B is its fast performance, which makes it suitable for real-time applications and large-scale language processing tasks. Additionally, it has been praised for its ability to handle complex language understanding and generation, making it a versatile choice for developers and researchers.
Benchmark: The model has been benchmarked and compared with other LLMs, demonstrating its competitive performance and wide applicability. It has been tested in various language-specific contexts, such as German, and has shown promising results in terms of understanding and generating language content.
Description: RWKV v.5 is an open-source large language model (LLM) developed by Recursal.AI. It is an RNN (recurrent neural network) with transformer-level performance, aiming to combine the strengths of RNNs and transformers, such as great performance, fast inference, low VRAM usage, fast training, "infinite" context length, and free sentence embedding. The model is trained on 100+ world languages, with a distribution of 70% English, 15% multilingual, and 15% code
Strengths: The strengths of RWKV v.5 include its ability to handle a wide range of languages, its performance comparable to transformer models, and its efficient use of resources such as VRAM during both training and inference.
Benchmark: RWKV v.5 has been benchmarked against other language models, demonstrating its competitive performance and its potential to handle diverse language tasks. It has shown promising results in terms of language understanding and generation, especially across multilingual contexts.
Each of these models represents a unique aspect of AI capabilities, tailored for different tasks and applications. For a deeper dive into their specific features and to explore their full potential!
Yes, and you can create any AI Apps with these Open Source models on Anakin AI, customize your workflow, and build up your own AI Agents with No Code!
Build Customized AI Agents with No Code at Anakin AI
The trajectory of open-source LLMs is towards more inclusive, ethical, and diverse AI models. Future developments are likely to focus on reducing biases in AI, improving the efficiency of models, and ensuring they are accessible to a broader range of users. Additionally, we can expect to see more collaboration between academia and industry, driving innovation and ethical standards in AI development.
Another exciting prospect is the evolution of models that can understand and generate not just text, but also other forms of data like images and audio, leading to more integrated and versatile AI systems.
Conclusion
The landscape of open-source Large Language Models is rich and varied, offering a plethora of tools for different applications. Each model brings something unique to the table. As AI continues to advance, these open-source models play a crucial role in democratizing AI technology, fostering innovation, and paving the way for more ethical and inclusive AI development.
In conclusion, the exploration of these open-source LLMs reveals a vibrant and dynamic field, brimming with potential and possibilities. As these models evolve, they promise to transform how we interact with technology, making AI more accessible and impactful in our daily lives.
