Instructions to use yashss/Phi-3-mini-128k-instruct_fineTuned_c4e18d4e-3885-445e-8714-27806494baf3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yashss/Phi-3-mini-128k-instruct_fineTuned_c4e18d4e-3885-445e-8714-27806494baf3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-128k-instruct") model = PeftModel.from_pretrained(base_model, "yashss/Phi-3-mini-128k-instruct_fineTuned_c4e18d4e-3885-445e-8714-27806494baf3") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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---
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base_model: microsoft/Phi-3-mini-128k-instruct
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library_name: peft
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license: mit
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tags:
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- trl
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- sft
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- generated_from_trainer
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model-index:
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- name: Phi-3-mini-128k-instruct_fineTuned_c4e18d4e-3885-445e-8714-27806494baf3
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Phi-3-mini-128k-instruct_fineTuned_c4e18d4e-3885-445e-8714-27806494baf3
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This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- training_steps: 2
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### Training results
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### Framework versions
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- PEFT 0.10.0
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- Transformers 4.39.3
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- Pytorch 2.3.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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adapter_model.safetensors
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