Instructions to use aCuteTrauma/lora_model_llmpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aCuteTrauma/lora_model_llmpc with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aCuteTrauma/lora_model_llmpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from aCuteTrauma/lora_model_llmpc: direct link, hf CLI and curl.
- Browser
- Download file 612 Bytes
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https://huggingface.co/aCuteTrauma/lora_model_llmpc/resolve/main/README.md
- Command line
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hf download hf://aCuteTrauma/lora_model_llmpc/README.md
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curl -L -o README.md https://huggingface.co/aCuteTrauma/lora_model_llmpc/resolve/main/README.md
612 Bytes
metadata
base_model: LLM-PBE/Llama3.1-8b-instruct-LLMPC-Blue-Team
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
Uploaded model
- Developed by: aCuteTrauma
- License: apache-2.0
- Finetuned from model : LLM-PBE/Llama3.1-8b-instruct-LLMPC-Blue-Team
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
