# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("SweatyCrayfish/Linux-CodeLlama-2-7B")
model = AutoModel.from_pretrained("SweatyCrayfish/Linux-CodeLlama-2-7B")Quick Links
Linux_Llama_Chat_7B
Kinux_Llama_Chat_7B is a fine-tuned model based on Llama 2 Chat 7b base model and fine-tuned on the data set Ubuntu Dialogue Corpus
Acknowledgments
Base Model: Llama-2-7b-chat-hf
- We utilized the Llama2 Chat 7b model as the base model for our project. The model was obtained from meta-llama/Llama-2-2b-chat-hf.
- Special thanks to AI at Meta for providing the model and the community around it for the support.
- License: A custom commercial license is available at: https://ai.meta.com/resources/models-and-libraries/llama-downloads/.
Fine-Tune Dataset
- The fine-tuning was performed on Ubuntu Dialogue Corpus dataset, which was crucial for achieving the results.
- The dataset is provided under Apache License, 2.0 license. We thank Ryan Lowe, Nissan Pow , Iulian V. Serban, and Joelle Pineau for making the dataset publicly available.
- Ryan Lowe, Nissan Pow, Iulian V. Serban and Joelle Pineau, "The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems", SIGDial 2015. URL: http://www.sigdial.org/workshops/conference16/proceedings/pdf/SIGDIAL40.pdf
- Downloads last month
- 70
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SweatyCrayfish/Linux-CodeLlama-2-7B")