phi-2-gpo-v30-i1
This model is a fine-tuned version of DUAL-GPO/phi-2-gpo-newSFT-b0.001-i0 on the HuggingFaceH4/ultrafeedback_binarized 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- total_eval_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
- Downloads last month
- 6
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for DUAL-GPO-2/phi-2-gpo-v30-i1
Base model
DUAL-GPO/phi-2-sft-lora-ultrachat-merged
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("DUAL-GPO/phi-2-sft-lora-ultrachat-merged") model = PeftModel.from_pretrained(base_model, "DUAL-GPO-2/phi-2-gpo-v30-i1")