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
metadata
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
