Instructions to use likithyadavv/codementor-v2-fullstack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use likithyadavv/codementor-v2-fullstack with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "likithyadavv/codementor-v2-fullstack") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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language:
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- en
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base_model:
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- 🤗 **Model Repo:** [likithyadavv/codementor-v2-fullstack](https://huggingface.co/likithyadavv/codementor-v2-fullstack)
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- 📖 **Base Model:** [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)
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- 🏫 **Institution:** MVJ College of Engineering, Bengaluru, India
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license: apache-2.0
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language:
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- en
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base_model:
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- likithyadavv/codementor-7b
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- 🤗 **Model Repo:** [likithyadavv/codementor-v2-fullstack](https://huggingface.co/likithyadavv/codementor-v2-fullstack)
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- 📖 **Base Model:** [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)
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- 🏫 **Institution:** MVJ College of Engineering, Bengaluru, India
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