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Antoinelyset

Models by this creator

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openhermes-2-mistral-7b-awq

antoinelyset

Total Score

146

The openhermes-2-mistral-7b-awq is a large language model developed by antoinelyset on the Replicate platform. It is similar to other Mistral language models like mistral-7b-v0.1, mistral-7b-instruct-v0.1, and mistral-7b-instruct-v0.2, as well as the llava-v1.6-mistral-7b model. It is also related to the nous-hermes-2-yi-34b-gguf model. Model inputs and outputs The openhermes-2-mistral-7b-awq model takes in the following inputs: Inputs prompt**: The JSON stringified of the messages (array of objects with role/content like OpenAI) to predict on temperature**: Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value max_new_tokens**: Max new tokens top_k**: When decoding text, samples from the top k most likely tokens; lower to ignore less likely tokens top_p**: When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens use_beam_search**: Whether to use beam search instead of sampling The model outputs an array of strings, which can be concatenated to form the final output. Outputs Output**: An array of strings representing the model's generated text Capabilities The openhermes-2-mistral-7b-awq model is a capable language model that can generate coherent and contextual text on a variety of topics. It can be used for tasks such as text generation, language translation, and summarization. What can I use it for? The openhermes-2-mistral-7b-awq model can be used for a wide range of natural language processing tasks, such as content creation, chatbots, and language generation. Companies could potentially use this model to automate tasks like customer service, content writing, and even product development. Things to try With the openhermes-2-mistral-7b-awq model, you can experiment with different temperature and top-k/top-p settings to see how they affect the coherence and creativity of the generated text. You can also try using beam search instead of sampling to see if it produces more consistent outputs. Additionally, you could fine-tune the model on a specific domain or task to see if it improves the model's performance for your particular use case.

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Updated 5/17/2024

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openhermes-2.5-mistral-7b

antoinelyset

Total Score

11

openhermes-2.5-mistral-7b is a large language model based on the Mistral-7B model, developed by the creator antoinelyset. It is a 7 billion parameter model that has been further fine-tuned and optimized for various tasks. This model builds on the capabilities of similar Mistral-7B models, such as mistral-7b-v0.1, mistral-7b-instruct-v0.1, and mistral-7b-instruct-v0.2, which have shown strong performance on a variety of language tasks. Model inputs and outputs The openhermes-2.5-mistral-7b model takes a JSON-formatted prompt as input, which can contain an array of messages with role and content information. The model can then generate new text based on the provided prompt. The output is an array of strings, where each string represents a generated response. Inputs prompt**: The JSON-formatted prompt containing an array of messages to predict on. temperature**: Adjusts the randomness of the outputs, with higher values resulting in more diverse and unpredictable text. top_k**: Specifies the number of most likely tokens to consider during the decoding process, allowing for more or less diversity in the output. top_p**: Specifies the percentage of most likely tokens to consider during the decoding process, also affecting the diversity of the output. max_new_tokens**: Determines the maximum number of new tokens the model will generate. Outputs An array of generated text, with each element representing a single response. Capabilities The openhermes-2.5-mistral-7b model is capable of engaging in open-ended conversations, generating coherent and contextually appropriate responses. It can be used for a variety of language-based tasks, such as text summarization, question answering, and content generation. What can I use it for? The openhermes-2.5-mistral-7b model can be used for a wide range of applications that involve natural language processing and generation. Some potential use cases include: Conversational AI**: The model can be integrated into chatbots, virtual assistants, and other conversational interfaces to provide human-like responses. Content Generation**: The model can be used to generate various types of text, such as articles, stories, or product descriptions. Summarization**: The model can be used to summarize longer pieces of text, distilling the key information and insights. Question Answering**: The model can be used to answer questions on a wide range of topics, drawing from its broad knowledge base. Things to try One interesting aspect of the openhermes-2.5-mistral-7b model is its ability to generate diverse and creative responses. By adjusting the temperature and top-k/top-p parameters, you can experiment with the level of randomness and variety in the output. This can be particularly useful for tasks like story generation or open-ended brainstorming, where you want to explore a range of possible ideas and directions. Additionally, you can try fine-tuning the model on domain-specific data to further specialize its capabilities for your particular use case. This can involve updating the model's parameters or incorporating additional training data to enhance its performance on specific tasks or topics.

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Updated 5/17/2024