Claire-7B-0.1
Maintainer: OpenLLM-France
43
🏋️
| 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
Claire-7B-0.1 is a 7B parameter causal decoder-only language model built by LINAGORA and OpenLLM-France. It was adapted from the Falcon-7b model and fine-tuned on French conversational data. Quantized versions of the model in GGUF format can be found in the TheBloke/Claire-7B-0.1-GGUF repository.
Model inputs and outputs
Inputs
- Text prompts for language generation, which can be in the form of open-ended queries, conversations, or instructions.
Outputs
- Continuations of the input text, generated by the model to continue the dialogue or complete the task.
Capabilities
Claire-7B-0.1 is designed to be adept at generating natural-sounding dialogue and handling conversational interactions. Without further fine-tuning, it is well-suited for tasks like chat-based applications, meeting summarization, and other dialogue-oriented use cases. The model is also capable of generating text on a wide range of topics, though its performance may be more variable outside of its core conversational domain.
What can I use it for?
The Claire-7B-0.1 model can be used as a foundation for building conversational AI applications, such as chatbots, digital assistants, or dialogue systems. It could also be fine-tuned for tasks like meeting summarization, response generation, and other language-based applications that involve interactive exchanges. The model's French-language focus makes it particularly well-suited for use cases targeting French-speaking audiences.
Things to try
One interesting aspect of Claire-7B-0.1 is its ability to generate disfluencies and other characteristics of spoken language, which can make the model's outputs feel more natural and human-like in conversational contexts. Developers could experiment with prompting the model to engage in back-and-forth dialogues, and observe how it handles the flow and dynamics of the interaction.
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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