Instructions to use Mukesh0606/solidity-codellama-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Mukesh0606/solidity-codellama-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("AlfredPros/CodeLlama-7b-Instruct-Solidity") model = PeftModel.from_pretrained(base_model, "Mukesh0606/solidity-codellama-qlora") - Notebooks
- Google Colab
- Kaggle
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| "best_model_checkpoint": null, | |
| "epoch": 0.15196413646379456, | |
| "eval_steps": 500, | |
| "global_step": 2000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
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| "epoch": 0.07598206823189728, | |
| "grad_norm": 0.30169111490249634, | |
| "learning_rate": 0.0004210970464135021, | |
| "loss": 1.6575, | |
| "mean_token_accuracy": 0.59712015157938, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 0.15196413646379456, | |
| "grad_norm": 0.33283600211143494, | |
| "learning_rate": 0.0004994442522809609, | |
| "loss": 1.538, | |
| "mean_token_accuracy": 0.6154271116852761, | |
| "step": 2000 | |
| } | |
| ], | |
| "logging_steps": 1000, | |
| "max_steps": 39483, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 1000, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 2.23245952321536e+16, | |
| "train_batch_size": 1, | |
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| "trial_params": null | |
| } | |