Instructions to use AmrBelal021/CodeGuard-7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AmrBelal021/CodeGuard-7B-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "AmrBelal021/CodeGuard-7B-v1") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AmrBelal021/CodeGuard-7B-v1: direct link, hf CLI and curl.
- Browser
- Download file 785 Bytes
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https://huggingface.co/AmrBelal021/CodeGuard-7B-v1/resolve/main/README.md
- Command line
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hf download hf://AmrBelal021/CodeGuard-7B-v1/README.md
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curl -L -o README.md https://huggingface.co/AmrBelal021/CodeGuard-7B-v1/resolve/main/README.md
785 Bytes
| library_name: peft | |
| base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit | |
| tags: | |
| - llama-3 | |
| - peft | |
| - lora | |
| - security | |
| - dpo | |
| model_creator: AmrBelal021 | |
| model_type: llama | |
| pipeline_tag: text-generation | |
| --- | |
| base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit | |
| tags: | |
| - text-generation-inference | |
| - transformers | |
| - unsloth | |
| - llama | |
| - trl | |
| license: apache-2.0 | |
| language: | |
| - en | |
| --- | |
| # Uploaded model | |
| - **Developed by:** AmrBelal021 | |
| - **License:** apache-2.0 | |
| - **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit | |
| This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) | |
| [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) | |