airoboros-l2-70b-gpt4-1.4.1
Maintainer: jondurbin
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The airoboros-l2-70b-gpt4-1.4.1 model is a large language model developed by Jon Durbin. It is a fine-tuned Llama 2 model with 70 billion parameters, trained on a dataset generated using the airoboros tool. This model builds on the previous Llama 65B GPT4 1.4 model from the same creator. Similar models include the airoboros-l2-70b-gpt4-1.4.1-GPTQ model, which provides GPTQ-quantized versions of the model for improved efficiency.
Model inputs and outputs
Inputs
- The model takes in text prompts as input.
Outputs
- The model generates coherent and contextual text as output.
Capabilities
The airoboros-l2-70b-gpt4-1.4.1 model is capable of generating human-like text across a wide range of topics. It can be used for tasks like language generation, text summarization, and question answering. The model has been fine-tuned to provide more detailed and informative responses compared to the previous Llama 65B model.
What can I use it for?
The airoboros-l2-70b-gpt4-1.4.1 model could be used for a variety of natural language processing applications, such as chatbots, content generation, or data analysis. However, due to the licensing restrictions around the use of the OpenAI API data used to fine-tune the model, it is recommended to avoid using this model commercially. The model could be a useful research tool or for personal/non-commercial projects.
Things to try
Experiment with different prompting techniques to see how the model responds to more specific or creative inputs. Try providing the model with instructions or tasks to see how it can follow directions and generate relevant text. Additionally, explore the model's ability to engage in conversational exchanges by providing it with back-and-forth dialogues.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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airoboros-llama-2-70b
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airoboros-llama-2-70b is a large language model with 70 billion parameters, created by fine-tuning the base Llama 2 model from Meta on a dataset curated by Jon Durbin. This model is part of the Airoboros series of LLMs, which also includes the Airoboros Llama 2 70B GPT4 1.4.1 - GPTQ and Goliath 120B models. The Airoboros models are designed for improved performance and safety compared to the original Llama 2 series. Model inputs and outputs Inputs prompt**: The text prompt for the model to continue seed**: A seed value for reproducibility, -1 for a random seed top_k**: The number of top candidates to keep during sampling top_p**: The top cumulative probability to filter candidates during sampling temperature**: The temperature of the output, best kept below 1 repetition_penalty**: The penalty for repeated tokens in the model's output max_tokens**: The maximum number of tokens to generate min_tokens**: The minimum number of tokens to generate use_lora**: Whether to use LoRA for prediction Outputs An array of strings representing the generated text Capabilities The airoboros-llama-2-70b model has the capability to engage in open-ended dialogue, answer questions, and generate coherent and contextual text across a wide range of topics. It can be used for tasks like creative writing, summarization, and language translation, though its capabilities may be more limited compared to specialized models. What can I use it for? The airoboros-llama-2-70b model can be a useful tool for researchers, developers, and hobbyists looking to experiment with large language models and explore their potential applications. Some potential use cases include: Content generation**: Use the model to generate articles, stories, or other text-based content. Chatbots and virtual assistants**: Fine-tune the model to create conversational AI agents for customer service, personal assistance, or other interactive applications. Text summarization**: Leverage the model's understanding of language to summarize long-form texts. Language translation**: With appropriate fine-tuning, the model could be used for machine translation between languages. Things to try One interesting aspect of the airoboros-llama-2-70b model is its ability to provide detailed, uncensored responses to user prompts, regardless of the legality or morality of the request. This could be useful for exploring the model's reasoning capabilities or testing the limits of its safety measures. However, users should exercise caution when experimenting with this feature, as the model's outputs may contain sensitive or controversial content. Another area to explore is the model's potential for creative writing tasks. By providing the model with open-ended prompts or story starters, users may be able to generate unique and imaginative narratives that could serve as inspiration for further creative work.
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