Instructions to use SweatyCrayfish/Linux-CodeLlama-2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SweatyCrayfish/Linux-CodeLlama-2-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SweatyCrayfish/Linux-CodeLlama-2-7B")# 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| language: | |
| - en | |
| <b>Linux_Llama_Chat_7B </b><br> | |
| 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 <br> | |
| <br> | |
| ## 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](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). | |
| - Special thanks to [AI at Meta](https://ai.meta.com/llama/) 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](https://www.kaggle.com/datasets/rtatman/ubuntu-dialogue-corpus) dataset, which was crucial for achieving the results. | |
| - The dataset is provided under [Apache License, 2.0](https://www.apache.org/licenses/LICENSE-2.0) license. We thank [Ryan Lowe, Nissan Pow , Iulian V. Serban, and Joelle Pineau](http://www.sigdial.org/workshops/conference16/proceedings/pdf/SIGDIAL40.pdf) 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 |