falcon-7b-sft-mix-2000
Maintainer: OpenAssistant
42
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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 falcon-7b-sft-mix-2000 model is a fine-tuned version of the Falcon 7B large language model, developed by the OpenAssistant team. This model was trained on a mixture of OASST top-2 threads, Dolly-15k, and synthetic instruction datasets, with the goal of improving its conversational and task-completion abilities.
Model inputs and outputs
The falcon-7b-sft-mix-2000 model takes in text prompts and generates continuations of that text. The model uses special tokens to mark the beginning of user and assistant turns, with each turn ending in an <|endoftext|> token.
Inputs
- Text prompts in a conversational format, with user and assistant turns marked by
<|prompter|>and<|assistant|>tokens
Outputs
- Continuations of the input text, generated by the model to continue the conversation or complete the task
Capabilities
The falcon-7b-sft-mix-2000 model has been fine-tuned to have improved conversational and task-completion abilities compared to the base Falcon 7B model. It can engage in open-ended dialogues, answer questions, and assist with a variety of tasks such as writing, analysis, and problem-solving.
What can I use it for?
The falcon-7b-sft-mix-2000 model could be useful for building conversational AI applications, such as virtual assistants, chatbots, or interactive educational tools. Its broad knowledge and language understanding capabilities make it a versatile model that could be applied to a range of use cases, from customer service to creative writing assistance.
Things to try
One interesting thing to try with the falcon-7b-sft-mix-2000 model is to engage it in open-ended conversations on a variety of topics, and see how it responds. Its fine-tuning on the OASST dataset may give it a more natural and engaging conversational style compared to the base Falcon 7B model. You could also try prompting it with specific tasks or challenges to see how it performs.
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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