Model save

README.md

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+---
+library_name: peft
+base_model: swkong/Phi-3-medium-128k-instruct-gptq-pubmed_unlabeled
+tags:
+- trl
+- sft
+- generated_from_trainer
+model-index:
+- name: Adapter-Phi-3-medium-128k-instruct-gptq-pubmed_unlabeled-lora-S50-E9
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Adapter-Phi-3-medium-128k-instruct-gptq-pubmed_unlabeled-lora-S50-E9
+
+This model is a fine-tuned version of [swkong/Phi-3-medium-128k-instruct-gptq-pubmed_unlabeled](https://huggingface.co/swkong/Phi-3-medium-128k-instruct-gptq-pubmed_unlabeled) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 9
+
+### Training results
+
+
+
+### Framework versions
+
+- PEFT 0.13.2
+- Transformers 4.46.2
+- Pytorch 2.4.0+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3