Instructions to use izzcw/llama_3_70b_lora_sft_construction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use izzcw/llama_3_70b_lora_sft_construction with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-70B-Instruct") model = PeftModel.from_pretrained(base_model, "izzcw/llama_3_70b_lora_sft_construction") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +3 -2
- all_results.json +12 -0
- eval_results.json +7 -0
- train_results.json +8 -0
- trainer_state.json +107 -0
- training_eval_loss.png +0 -0
- training_loss.png +0 -0
README.md
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base_model: meta-llama/Meta-Llama-3-70B-Instruct
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tags:
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- llama-factory
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- generated_from_trainer
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model-index:
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- name: llama_3_70b_lora_sft_construction
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# llama_3_70b_lora_sft_construction
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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base_model: meta-llama/Meta-Llama-3-70B-Instruct
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tags:
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- llama-factory
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- lora
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- generated_from_trainer
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model-index:
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- name: llama_3_70b_lora_sft_construction
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# llama_3_70b_lora_sft_construction
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on the identity and the filtered_construction_train_data datasets.
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It achieves the following results on the evaluation set:
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- Loss: 0.3475
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## Model description
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all_results.json
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"train_steps_per_second": 0.011
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}
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eval_results.json
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"eval_steps_per_second": 0.623
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}
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train_results.json
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}
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trainer_state.json
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training_eval_loss.png
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training_loss.png
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