metadata
library_name: peft
license: mit
base_model: microsoft/Phi-3.5-mini-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: curator_math_phase1_sn_ensemble7_90325
results: []
See axolotl config
axolotl version: 0.5.0
Collinear Curator 1:
This is an open-source fine-tuned reasoning adapter of microsoft/Phi-3.5-mini-instruct, transformed into a math reasoning model using data curated from collinear-ai/R1-Distill-SFT-Curated. It achieves the following results on the evaluation set:
- Loss: 0.3203
Model description
This model is a LoRA adaptor and for best results merge it with base model microsoft/Phi-3.5-mini-instruct before use.
Training and evaluation data
- Training data: collinear-ai/R1-Distill-SFT-Curated
- Evaluation data: HuggingFaceH4/MATH-500
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0003 | 1 | 0.6714 |
| 0.337 | 0.3335 | 1243 | 0.3361 |
| 0.3248 | 0.6669 | 2486 | 0.3203 |
Evaluation Results on Math500
The following figure shows the accuracy and the speedup of Collinear Curators C1 and C2 when compared to training on unfiltered dataset.
Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3