Instructions to use bunnycore/Llama-3.2-3B-Code-lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bunnycore/Llama-3.2-3B-Code-lora_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bunnycore/Llama-3.2-3B-Code-lora_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Uploaded model
dataset = load_dataset("Crystalcareai/Code-feedback-sharegpt-renamed", split = "train")
dataset2 = load_dataset("MaziyarPanahi/Synthia-Coder-v1.5-I-sharegpt", split = "train")
dataset3 = load_dataset("Alignment-Lab-AI/CodeInterpreterData-sharegpt", split = "train")
dataset4 = load_dataset("adamo1139/m-a-p_CodeFeedback_norefusals_ShareGPT", split = "train")
dataset5 = load_dataset("mahiatlinux/Glaive-code-assistant-ShareGPT", split = "train")
dataset6 = load_dataset("Nitral-AI/Olympiad_Math-ShareGPT", split = "train")
- Developed by: bunnycore
- License: apache-2.0
- Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
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