Openpipe
Models by this creator
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mistral-ft-optimized-1218
151
The mistral-ft-optimized-1218 model is a fine-tuned version of the Mistral-7B base model, developed by OpenPipe. It is intended to be a strong base model suitable for downstream fine-tuning on a variety of tasks. Benchmark results show the model outperforms many other Mistral fine-tuned models and even the original Mistral-7B on a range of metrics, making it a compelling option for those looking to build upon a capable foundation. Similar models include the OpenHermes-2.5-Mistral-7B and NeuralHermes-2.5-Mistral-7B, which build on the same Mistral-7B base and showcase the versatility of this architecture. Model inputs and outputs Inputs Text**: The mistral-ft-optimized-1218 model is a text-to-text transformer, accepting natural language text as input. Outputs Text**: The model generates coherent, fluent text as output, making it suitable for a wide range of natural language processing tasks. Capabilities The mistral-ft-optimized-1218 model demonstrates strong performance across a variety of benchmarks, including GPT4All, AGIEval, BigBench, and TruthfulQA. It excels at tasks like open-ended question answering, logical reasoning, and language understanding. The model is particularly adept at generating relevant, informative responses to prompts, drawing upon its broad knowledge base. What can I use it for? The mistral-ft-optimized-1218 model's versatility makes it a valuable tool for a wide range of applications, from content generation and text summarization to dialogue systems and language-based AI assistants. Its strong base performance allows for efficient fine-tuning on specific tasks, making it an attractive option for developers and researchers looking to build custom models without starting from scratch. Things to try One interesting aspect of the mistral-ft-optimized-1218 model is its ability to engage in multi-turn dialogue, thanks to its support for the ChatML prompt format. By utilizing system prompts, users can instruct the model to take on different personas or roles, unlocking novel use cases and interactions. Additionally, the model's strong performance on tasks like code generation and understanding suggests it could be a valuable tool for developers, potentially assisting with tasks like code completion, debugging, and even algorithm design.
Updated 5/27/2024
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mistral-ft-optimized-1227
77
The mistral-ft-optimized-1227 model is an AI model developed by OpenPipe. It is a hierarchical SLERP merge of several base models, including teknium/OpenHermes-2.5-Mistral-7B, Intel/neural-chat-7b-v3-3, meta-math/MetaMath-Mistral-7B, and openchat/openchat-3.5-1210. This model is intended to be a strong base suitable for downstream fine-tuning on a variety of tasks, and according to the maintainer's internal evaluations, it is one of the strongest models for most downstream tasks. The similar mistral-ft-optimized-1218 model was previously released, but the maintainer recommends the updated mistral-ft-optimized-1227 model as it has similar performance and a more permissive license. Model inputs and outputs Inputs Text**: The model can take in text-based inputs for a variety of tasks. Outputs Text**: The model generates text-based outputs, which can include responses to questions, generated content, and more. Capabilities The mistral-ft-optimized-1227 model demonstrates strong performance across a range of benchmarks, including GPT4All, AGIEval, BigBench, and TruthfulQA. It outperforms previous versions of the Mistral model as well as many other current Mistral fine-tunes. What can I use it for? The mistral-ft-optimized-1227 model can be used for a variety of text-based tasks, such as question answering, content generation, and language understanding. The maintainer's description suggests it is particularly well-suited for downstream fine-tuning on a variety of tasks, making it a versatile base model for further development. Things to try One interesting aspect of the mistral-ft-optimized-1227 model is its ability to perform well on both code-related and non-code-related tasks. The maintainer's description notes that training the model on a good ratio of code datasets has boosted its performance on several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All. This suggests the model has developed a strong general-purpose language understanding capability that could be leveraged for a wide range of applications.
Updated 5/28/2024