Instructions to use neerajsp23/mistral-finetuned-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use neerajsp23/mistral-finetuned-samsum with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ") model = PeftModel.from_pretrained(base_model, "neerajsp23/mistral-finetuned-samsum") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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- name: mistral-finetuned-samsum
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results: []
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- training_steps: 250
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- mixed_precision_training: Native AMP
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### Training results
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### Framework versions
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license: apache-2.0
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library_name: peft
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tags:
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- trl
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- sft
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base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
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model-index:
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- name: mistral-finetuned-samsum
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results: []
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- training_steps: 250
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### Training results
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### Framework versions
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- PEFT 0.7.1
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- Transformers 4.37.0.dev0
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- Pytorch 2.1.0+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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