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--- |
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license: llama2 |
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language: |
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- en |
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--- |
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# **Model Overview** |
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As the demand for large language models grows, a common limitation surfaces: their inability to directly search the internet. Although tech giants like Google (with Bard), Bing, and Perplexity are addressing this challenge, their proprietary methods have data logging issues. |
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**Introducing Open LLM Search** — A specialized adaptation of Together AI's `llama-2-7b-32k` model, purpose-built for extracting information from web pages. While the model only has a 7 billion parameters, its fine-tuned capabilities and expanded context limit enable it to excel in search tasks. |
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**License:** This model uses Meta's Llama 2 license. |
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# **Fine-Tuning Process** |
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The model's fine tuning involved a combination of GPT-4 and GPT-4-32k to generate synthetic data. Here is the training workflow used: |
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1. Use GPT-4 to generate a multitude of queries. |
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2. For each query, identify the top five website results from Google. |
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3. Extract content from these websites and use GPT-4-32k for their summarization. |
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4. Record the text and summarizes from GPT-4-32k for fine-tuning. |
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5. Feed the summaries from all five sources with GPT-4 to craft a cohesive response. |
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6. Document both the input and output from GPT-4 for fine-tuning. |
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Fine tuning was done with an `<instructions>:`, `<user>:`, and `<assistant>:` format. |
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# **Getting Started** |
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- Experience it firsthand! Check out the live demo [here](https://huggingface.co/spaces/masonbarnes/open-llm-search). |
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- For DIY enthusiasts, explore or self-deploy this solution using our [GitHub repository](https://github.com/MasonBarnes/open-llm-search). |
