Instructions to use HugManish22/mbgpt-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HugManish22/mbgpt-ft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.2-GPTQ") model = PeftModel.from_pretrained(base_model, "HugManish22/mbgpt-ft") - Notebooks
- Google Colab
- Kaggle
File size: 1,966 Bytes
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base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: MBGPT-ft
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. -->
# MBGPT-ft
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8936
## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.5929 | 0.9231 | 3 | 3.9618 |
| 4.0418 | 1.8462 | 6 | 3.4346 |
| 3.4614 | 2.7692 | 9 | 2.9810 |
| 2.253 | 4.0 | 13 | 2.5605 |
| 2.666 | 4.9231 | 16 | 2.3088 |
| 2.3577 | 5.8462 | 19 | 2.1243 |
| 2.1787 | 6.7692 | 22 | 2.0194 |
| 1.5374 | 8.0 | 26 | 1.9470 |
| 2.0037 | 8.9231 | 29 | 1.9060 |
| 1.3905 | 9.2308 | 30 | 1.8936 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.42.4
- Pytorch 2.1.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |