Update README.md

README.md

@@ -8,13 +8,13 @@ language:
 
 # Model Card for Model ID
 
-Original model [elyza/ELYZA-japanese-Llama-2-7b-instruct](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-instruct) which is based on Meta's "Llama 2" and has undergone additional pre-training in Japanese, and thier original post-training and speed up tuning.
+Original model [elyza/ELYZA-japanese-Llama-2-7b-instruct](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-instruct) which is based on Meta's "Llama 2" and has undergone additional pre-training in Japanese instruction.
 
-This model is a AWQ quantized(miniaturized to 3.8GB) version of the original model(13.48GB).  
+This model is a AWQ quantized(miniaturized to 3.89GB) version of the original model(13.48GB).  
 
 ## Model Details
 
-Currently, this model **only works with the Colab A100** or RTX series. Even though there is enough GPU memory, the output may become abnormal on T4 and V100. The cause of the abnormal output has not yet been determined. 
+Currently, this model is confirmed to work with **Colab A100** or RTX 3060 on local PC. There is a problem with Free Colab(T4) and Colab Pro(V100) that prevents text from being output even though there is sufficient GPU memory. However, I still don't know the cause.
 
 Quantization reduces the amount of memory required and improves execution speed, but unfortunately performance deteriorates.  
 
@@ -22,7 +22,7 @@ In particular, the original model is tuned for the purpose of strengthening the
 
 Although the ability to follow instructions cannot be measured using existing automated benchmarks, we have confirmed that quantized model significantly deteriorates the ability to follow instructions.
 
-This model has better ability to follow instructions than the [GPTQ version](https://huggingface.co/dahara1/ELYZA-japanese-Llama-2-7b-fast-instruct-GPTQ).
+But this model has better ability to follow instructions than the previous [GPTQ version](https://huggingface.co/dahara1/ELYZA-japanese-Llama-2-7b-fast-instruct-GPTQ).
 
 ## Sample Script
 
@@ -95,5 +95,34 @@ Output
 ふなっしーはリラックマに船橋を後にするよ
 ```
 
-## See also
+### Citations
+
+This model is based on the work of the following people:  
+
+```tex
+@misc{elyzallama2023, 
+      title={ELYZA-japanese-Llama-2-7b}, 
+      url={https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b}, 
+      author={Akira Sasaki and Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura},
+      year={2023},
+}
+```
+
+```tex
+@misc{touvron2023llama,
+      title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, 
+      author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
+      year={2023},
+      eprint={2307.09288},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+
+### about this work
+- **This Quantization work was done by :** [webbigdata](https://webbigdata.jp/)
+
+
+### See also
 [casper-hansen/AutoAWQ](https://github.com/casper-hansen/AutoAWQ)