Instructions to use avsolatorio/data-use-unsloth-phi-3.5-data2-100epochs-test-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avsolatorio/data-use-unsloth-phi-3.5-data2-100epochs-test-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("avsolatorio/data-use-unsloth-phi-3.5-data2-100epochs-test-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use avsolatorio/data-use-unsloth-phi-3.5-data2-100epochs-test-lora 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 avsolatorio/data-use-unsloth-phi-3.5-data2-100epochs-test-lora 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 avsolatorio/data-use-unsloth-phi-3.5-data2-100epochs-test-lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for avsolatorio/data-use-unsloth-phi-3.5-data2-100epochs-test-lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="avsolatorio/data-use-unsloth-phi-3.5-data2-100epochs-test-lora", max_seq_length=2048, )
{"epoch": 0, "global_step": 0, "max_steps": 38400, "logging_steps": 1, "eval_steps": 500, "save_steps": 500, "train_batch_size": 2, "num_train_epochs": 100, "num_input_tokens_seen": 0, "total_flos": 0, "log_history": [], "best_metric": null, "best_model_checkpoint": null, "is_local_process_zero": true, "is_world_process_zero": true, "is_hyper_param_search": false, "trial_name": null, "trial_params": null, "stateful_callbacks": {"TrainerControl": {"args": {"should_training_stop": false, "should_epoch_stop": false, "should_save": false, "should_evaluate": false, "should_log": false}, "attributes": {}}}} (Trained with Unsloth)
e1b153e verified - Xet hash:
- 91bf184ab12793d0754344f9095332759432e666320cc6c07f637af50e36db6f
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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