long_llama_3b
Maintainer: syzymon
119
⚙️
| 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
long_llama_3b is a large language model developed by syzymon, a researcher at Hugging Face. It is based on the OpenLLaMA model, which is an open-source reproduction of Meta's LLaMA model. The key difference is that long_llama_3b has been fine-tuned using the Focused Transformer (FoT) method to extend the maximum context length from 8k tokens to 256k tokens or more. This allows the model to handle much longer input text than the original LLaMA model.
The long_llama_3b model inherits the capabilities of the base OpenLLaMA model, which was trained on a large corpus of text data. It can be used for a variety of natural language processing tasks such as text generation, question answering, and summarization. The extended context length makes it particularly well-suited for applications that require understanding long-form documents or multiple related passages.
Model Inputs and Outputs
Inputs
- Text data, with a maximum context length of 256k tokens or more.
Outputs
- Generated text, with the model producing a probability distribution over the next token at each step.
Capabilities
The long_llama_3b model excels at handling long-form text inputs, allowing it to understand and reason about complex topics that span multiple paragraphs or pages. This capability is demonstrated in a key retrieval task, where the model was able to handle inputs of up to 256k tokens.
Compared to the original LLaMA model, long_llama_3b can generate more coherent and context-aware text, as it is able to better capture long-range dependencies in the input. This makes it a powerful tool for applications like long-form document summarization, where the model needs to understand the overall meaning and structure of a lengthy text.
What Can I Use It For?
The long_llama_3b model can be used for a variety of natural language processing tasks that benefit from the ability to handle long-form text inputs, such as:
- Long-form document summarization: Generating concise summaries of lengthy reports, articles, or books.
- Multi-document question answering: Answering questions that require information from multiple related passages.
- Long-form content generation: Producing coherent and context-aware long-form text, such as stories, essays, or academic papers.
- Conversational AI: Engaging in more natural and contextual dialogue, as the model can better understand the full conversation history.
Things to Try
One key aspect to explore with long_llama_3b is the impact of the context length on the model's performance. As mentioned, the model can handle much longer inputs than the original LLaMA model, but the optimal context length may vary depending on the specific task and dataset. Experimenting with different context lengths and observing the changes in model outputs can provide valuable insights into how the model utilizes long-range information.
Another interesting area to explore is the model's ability to handle long-form, multi-document inputs. By providing the model with related passages or documents, you can assess its capacity to synthesize information and generate coherent, context-aware responses. This could be particularly useful for tasks like long-form question answering or multi-document summarization.
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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