Update README.md

README.md

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----
-inference: false
-license: llama2
-tags:
-- QuantizingModel
-- 8bit
-- vicuna
----
-Quantizing Model - 8-bit 
-
-
----
-inference: false
-license: llama2
----
-
-# Vicuna Model Card
-
-## Model Details
-
+Quantizing Model - 8-bit
+Vicuna Model Card
+Model Details
 Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.
-
-- **Developed by:** [LMSYS](https://lmsys.org/)
-- **Model type:** An auto-regressive language model based on the transformer architecture
-- **License:** Llama 2 Community License Agreement	
-- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288)
-
-### Model Sources
-
-- **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
-
-The primary use of Vicuna is research on large language models and chatbots.
-The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
-
-## 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 v1.5 is fine-tuned from Llama 2 with supervised instruction fine-tuning.
-The training data is around 125K 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
-
-![Evaluation Results](https://github.com/lm-sys/lm-sys.github.io/blob/main/public/images/webdata/vicuna_v1.5_eval.png?raw=true)
-
-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).
-
-## Difference between different versions of Vicuna
-
-See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
+•	Developed by: Vigneshwaran D
+•	Model type: An auto-regressive language model based on the transformer architecture
+•	License: Llama 2 Community License Agreement
+•	Finetuned from model: Llama 2
+•	Quantized to: 8-bit
+Base Model Sources
+•	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
+The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
+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 v1.5 is fine-tuned from Llama 2 with supervised instruction fine-tuning. The training data is around 125K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this paper.
+Quantization Details
+This model has been quantized to 8-bit precision to reduce memory usage and improve inference speed, making it more efficient for deployment on devices with limited resources. The quantization process involves converting the model weights from 32-bit floating point to 8-bit integer representation.
+Evaluation
+ 
+Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this paper and leaderboard.
+Difference between different versions of Vicuna
+See vicuna_weights_version.md