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Ejafa_-_phi-3-mini-128k-instruct-dpo-lr-5e-07-4bits
RichardErkhov/Ejafa_-_phi-3-mini-128k-instruct-dpo-lr-5e-07-4bits
Ejafa_-_phi-3-mini-128k-instruct-dpo-lr-5e-07-4bits is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
phi-3-mini-128k-instruct-dpo-lr-5e-07 - bnb 4bits
- Model creator: https://huggingface.co/Ejafa/
- Original model: https://huggingface.co/Ejafa/phi-3-mini-128k-instruct-dpo-lr-5e-07/
Original model description:
license: mit base_model: microsoft/Phi-3-mini-128k-instruct tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer datasets:
- princeton-nlp/llama3-ultrafeedback model-index:
- name: phi-3-mini-128k-instruct-dpo-lr-5e-07 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. -->
Description
This model was trained as part of the Reinforcement Learning - 24 project at Peking University, focusing on [dpo].
Authors
- Ejafa Bassam
- Yaroslav Ponomarenko
phi-3-mini-128k-instruct-dpo-lr-5e-07
This model is a fine-tuned version of microsoft/Phi-3-mini-128k-instruct on the princeton-nlp/llama3-ultrafeedback dataset. It achieves the following results on the evaluation set:
- Loss: 0.6096
- Rewards/chosen: -1.0852
- Rewards/rejected: -1.4834
- Rewards/accuracies: 0.6976
- Rewards/margins: 0.3982
- Logps/rejected: -434.2651
- Logps/chosen: -403.4777
- Logits/rejected: 1.6861
- Logits/chosen: 1.6753
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-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- 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
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.62 | 0.8549 | 400 | 0.6104 | -1.0659 | -1.4533 | 0.6976 | 0.3875 | -433.6641 | -403.0910 | 1.6821 | 1.6709 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Type
- phi3
- Created
- Jan 23, 2025
- Updated
- Jan 23, 2025