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README.md

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+# **Model Overview**
+
+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.
+
+**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.
+
+**License:** This model uses Meta's Llama 2 license.
+
+# **Fine-Tuning Process**
+
+The model's fine tuning involved a synergy of GPT-4 and GPT-4-32k to generate synthetic data. Here is the training workflow used:
+1. Use GPT-4 to generate a multitude of queries.
+2. For each query, identify the top five website results from Google.
+3. Extract content from these websites and use GPT-4-32k for their summarization.
+4. Record the text and summarizes from GPT-4-32k for fine-tuning.
+5. Feed the summaries from all five sources with GPT-4 to craft a cohesive response.
+6. Document both the input and output from GPT-4 for fine-tuning.
+
+The fine-tuning adopted the format: `<instructions>:`, `<user>:`, and `<assistant>:`.
+
+# **Getting Started**
+
+- Experience it firsthand! Check out the live demo [here](https://google.com).
+- For DIY enthusiasts, explore or self-deploy this solution using our [GitHub repository](https://google.com).