Instructions to use tangera/Llama3-8B-Mob with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tangera/Llama3-8B-Mob with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tangera/Llama3-8B-Mob", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use tangera/Llama3-8B-Mob with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tangera/Llama3-8B-Mob to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tangera/Llama3-8B-Mob to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tangera/Llama3-8B-Mob to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="tangera/Llama3-8B-Mob", max_seq_length=2048, )
A pretrain B finetune
- Developed by: tangera
- License: apache-2.0
- Pretrained from model : unsloth/llama-3-8b-Instruct-bnb-4bit
- Finetuned from model : model-First:v14
- Continued Finetuning from model : model-B_eval_loss:v44
- Pretraining Epoch : 5.0 (0-1000)
- Finetuning Epoch : 1.0
- Continued Finetuning Epoch : 2.0
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
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Model tree for tangera/Llama3-8B-Mob
Base model
unsloth/llama-3-8b-Instruct-bnb-4bit