Update README.md

README.md

@@ -9,40 +9,45 @@ If you still want to use this old version, please see the compatibility and diff
 **NOTE: This "delta model" cannot be used directly.**  
 Users have to apply it on top of the original LLaMA weights to get actual Vicuna weights. See [instructions](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md#how-to-apply-delta-weights-for-weights-v11-and-v0).
 
-<br>
 <br>
 
 # Vicuna Model Card
 
-## Model details
-
-**Model type:**
-Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
-It is an auto-regressive language model, based on the transformer architecture.
+## Model Details
 
-**Model date:**
-Vicuna was trained between March 2023 and April 2023.
+Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
 
-**Organizations developing the model:**
-The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
+- **Developed by:** [LMSYS](https://lmsys.org/)
+- **Model type:** An auto-regressive language model based on the transformer architecture.
+- **License:** Non-commercial license
+- **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971).
 
-**Paper or resources for more information:**
-https://lmsys.org/blog/2023-03-30-vicuna/
+### Model Sources
 
-**Where to send questions or comments about the model:**
-https://github.com/lm-sys/FastChat/issues
+- **Repository:** https://github.com/lm-sys/FastChat
+- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
+- **Paper:** https://arxiv.org/abs/2306.05685
+- **Demo:** https://chat.lmsys.org/
 
+## Uses
 
-## Intended use
-**Primary intended uses:**
 The primary use of Vicuna is research on large language models and chatbots.
-
-**Primary intended users:**
 The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
 
-## Training dataset
-70K conversations collected from ShareGPT.com.
+## How to Get Started with the Model
+
+Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights.  
+APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api.  
+
+## Training Details
+
+Vicuna v1.1 is fine-tuned from LLaMA with supervised instruction fine-tuning.
+The training data is around 70K conversations collected from ShareGPT.com.
+See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
+
+## Evaluation
+
+Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
 
-## Evaluation dataset
-A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs.
-See https://lmsys.org/blog/2023-03-30-vicuna/ for more details.
+## Difference between different versions of Vicuna
+See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)