falcon-40b-sft-top1-560
Maintainer: OpenAssistant
49
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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-40b-sft-top1-560 model is a fine-tuning of TII's Falcon 40B large language model by the Open-Assistant team. It was trained on high-quality human demonstrations from the OASST dataset, with an effective batch size of 144 for approximately 7.5 epochs. The model has capabilities in English, German, Spanish, and French, with limited abilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, and Swedish.
Similar models from the Open-Assistant project include the oasst-sft-4-pythia-12b-epoch-3.5 and oasst-sft-1-pythia-12b models, which were fine-tuned on human demonstrations using the Pythia 12B model. The llama2-70b-oasst-sft-v10 and codellama-13b-oasst-sft-v10 models are fine-tunings of Meta's Llama2 70B and CodeLlama 13B models, respectively.
Model inputs and outputs
Inputs
- Natural language prompts in a variety of languages, including English, German, Spanish, and French.
- The model uses special tokens
<|prompter|>and<|assistant|>to mark the beginning of user and assistant turns, with each turn ending in<|endoftext|>.
Outputs
- The model generates natural language responses in the same languages as the input prompts, with the goal of providing helpful and informative answers.
- The output can span multiple paragraphs and include relevant information, insights, and recommendations based on the input prompt.
Capabilities
The falcon-40b-sft-top1-560 model is capable of engaging in open-ended conversations, answering questions, and providing explanations and analysis on a wide range of topics. It has shown strong performance on the OASST dataset, demonstrating its ability to generate coherent and contextually appropriate responses.
What can I use it for?
This model can be used in a variety of applications that require natural language understanding and generation, such as:
- Building interactive AI assistants or chatbots to help users with tasks and queries.
- Generating content for websites, blogs, or social media platforms.
- Providing language-based support or customer service.
- Aiding in research, analysis, or creative writing tasks.
The model's multilingual capabilities also make it suitable for use in international or global applications.
Things to try
One interesting aspect of the falcon-40b-sft-top1-560 model is its ability to provide nuanced and contextual responses. Try prompting the model with open-ended questions or scenarios that require it to draw upon a range of knowledge and reasoning skills. See how the model responds and how it compares to your own understanding or expectations.
Additionally, you can explore the model's versatility by attempting tasks or prompts that span different domains, such as answering questions about science, history, or current events, or generating creative fictional narratives. Observe how the model adapts and performs across these varied use cases.
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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