Curated Numinamath
Collection
3 items • Updated
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.
See axolotl version
axolotl version: 0.5.0
Intended uses & limitations
Math-Reasoning
Training and evaluation data
Training data curated from 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: 10
- eval_batch_size: 10
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 160
- total_eval_batch_size: 80
- 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 | 0.6646 |
| 0.3174 | 0.3335 | 1247 | 0.3329 |
| 0.307 | 0.6670 | 2494 | 0.3169 |
Evaluation on Math500
Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3
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Model tree for collinear-ai/math_reasoning_phi_c2
Base model
microsoft/Phi-3.5-mini-instruct
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct") model = PeftModel.from_pretrained(base_model, "collinear-ai/math_reasoning_phi_c2")