Instructions to use Howard881010/heat_transfer_sft_5000_mcq_1epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Howard881010/heat_transfer_sft_5000_mcq_1epoch with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-Nemo-Instruct-2407") model = PeftModel.from_pretrained(base_model, "Howard881010/heat_transfer_sft_5000_mcq_1epoch") - Notebooks
- Google Colab
- Kaggle
File size: 1,732 Bytes
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base_model: mistralai/Mistral-Nemo-Instruct-2407
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: heat_transfer_sft_5000_mcq_1epoch
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. -->
# heat_transfer_sft_5000_mcq_1epoch
This model is a fine-tuned version of [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) on the heat_transfer_5000_mcq dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0035
## 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: 10
- eval_batch_size: 10
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 40
- total_eval_batch_size: 40
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.004 | 0.2655 | 30 | 0.0038 |
| 0.0037 | 0.5310 | 60 | 0.0036 |
| 0.0035 | 0.7965 | 90 | 0.0035 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.46.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.1 |