llama2-13b-orca-8k-3319
Maintainer: OpenAssistant
131
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| Property | Value |
|---|---|
| Model Link | View 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 llama2-13b-orca-8k-3319 model is a fine-tuning of Meta's Llama2 13B model with an 8K context size, trained on a long-conversation variant of the Dolphin dataset called orca-chat. This extends the original Llama2 model's capabilities to handle longer contexts, which can be useful for applications like multi-document question answering and long-form summarization.
Similar models like the codellama-13b-oasst-sft-v10 from OpenAssistant and the orca_mini_3b from pankajmathur also build on the Llama2 base model with various fine-tunings and adaptations. The LLaMA-2-7B-32K model from Together Computer further extends the context length to 32K tokens.
Model inputs and outputs
Inputs
- Text prompt: The model can take in a text prompt of any length, up to the 8,192 token context limit.
Outputs
- Continuation text: The model will generate a continuation of the input text, producing a longer output sequence.
Capabilities
The llama2-13b-orca-8k-3319 model excels at generating coherent, contextual responses even for longer input prompts. This makes it well-suited for tasks like multi-turn conversations, where maintaining context over many exchanges is important. It can also be useful for applications that require understanding and summarizing longer-form content, such as research papers or novels.
What can I use it for?
This model could be used for a variety of language-based applications that benefit from handling longer input contexts, such as:
- Chatbots and dialog systems: The extended context length allows the model to maintain coherence and memory over longer conversations.
- Question answering systems: The model can draw upon more contextual information to provide better answers to complex, multi-part questions.
- Summarization tools: The model's ability to process longer inputs makes it suitable for summarizing lengthy documents or articles.
Things to try
An interesting experiment would be to fine-tune the llama2-13b-orca-8k-3319 model further on a specific task or domain, such as long-form text generation or multi-document QA. The model's strong performance on the Dolphin dataset suggests it could be a powerful starting point for building specialized language models.
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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