Update README.md

README.md

@@ -1,14 +1,14 @@
 ---
-license: mit
+license: apache-2.0
 ---
 
 # SciPhi-SearchAgent-Alpha-7B Model Card
 
-The SciPhi-SearchAgent-Alpha-7B is a Large Language Model (LLM) fine-tuned from Mistral-7B-v0.1. This model underwent a fine-tuning process using retrieval-augmented generation (RAG) over search with a fully synthetic dataset. The objective of this work is to generate accurate and well-cited summaries from a range of search results, providing more accurate answers to user queries. For best results, follow the prompting guidelines below.
+The SciPhi-SearchAgent-Alpha-7B is a Large Language Model (LLM) fine-tuned from Mistral-7B-v0.1. This model was fine tuned with a fully synthetic dataset to specialize at performing retrieval-augmented generation (RAG) over detailed web search results. This work aims to train an agent which specializes in using search engines such as [AgentSearch](https://huggingface.co/datasets/SciPhi/AgentSearch-V1) to generate accurate and well-cited summaries from a range of search results, providing more accurate answers to user queries. Please refer to the [docs here](https://agent-search.readthedocs.io/en/latest/) for more information on how to run the agent in practice.
 
-SciPhi-AI is available via a free hosted API, though the exposed model can vary. Currently, SciPhi-SearchAgent-Alpha-7B is available. More details can be found in the docs [here](https://agent-search.readthedocs.io/en/latest/).
+Currently, SciPhi-SearchAgent-Alpha-7B is available via hosted api at https://www.sciphi.ai. 
 
-The search can be accessed directly [here](https://search.sciphi.ai/). 
+You can try a demonstration of SearchAgent [here](https://search.sciphi.ai/). 
 
 ## Model Architecture
 
@@ -25,19 +25,33 @@ Base Model: Mistral-7B-v0.1
 
 It is recommended to use a single search query. The model will return an answer using search results as context. 
 
-In order to use the model, you can go to the website https://search.sciphi.ai/, or you can run it locally using the following simple command:
-
+Using the AgentSearch package an example is shown below.
 ```
 export SCIPHI_API_KEY=MY_SCIPHI_API_KEY
 # Use the SciPhi `SearchAgent` for LLM RAG w/ AgentSearch
 python -m agent_search.scripts.run_rag run --query="What is Fermat's last theorem?"
 ```
 
-See the documentation, linked above, for more information.
+Alternatively, you may provide your own search context directly to the model by adhereing to the following format:
+
+```
+### Instruction:
+Your task is to perform retrieval augmented generation (RAG) over the given query and search results. Return your answer with three sections `My Work`, `My Answer`, and `My Further Considerations`. 
+
+Query:
+{query}
+
+Search Results:
+{search_results}
+
+Query:
+{query}
+
+### Response:
+```
 
 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
 
 ## References
 
-1. Mistral AI. (2023). Model Card for Mistral-7B-v0.1. The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks tested. For full details, please refer to the paper and release blog post. Model Architecture: Transformer with Grouped-Query Attention, Sliding-Window Attention, and Byte-fallback BPE tokenizer. [Link](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-
+1. Mistral AI. (2023). Model Card for Mistral-7B-v0.1. The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks tested. For full details, please refer to the paper and release blog post. Model Architecture: Transformer with Grouped-Query Attention, Sliding-Window Attention, and Byte-fallback BPE tokenizer. [Link](https://huggingface.co/mistralai/Mistral-7B-v0.1)