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") - Notebooks
- Google Colab
- Kaggle
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README.md
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Ubuntu_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>
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language:
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<b>Linux_Llama_Chat_7B </b><br>
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Ubuntu_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>
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