Instructions to use MatanP/LLAMA_fine_tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MatanP/LLAMA_fine_tuned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "MatanP/LLAMA_fine_tuned") - Notebooks
- Google Colab
- Kaggle
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Download README.md from MatanP/LLAMA_fine_tuned: direct link, hf CLI and curl.
- Browser
- Download file 1.71 kB
-
https://huggingface.co/MatanP/LLAMA_fine_tuned/resolve/main/README.md
- Command line
-
hf download hf://MatanP/LLAMA_fine_tuned/README.md
-
curl -L -o README.md https://huggingface.co/MatanP/LLAMA_fine_tuned/resolve/main/README.md
1.71 kB
metadata
base_model: meta-llama/Llama-2-7b-hf
library_name: peft
license: llama2
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: SFT_FineTuned_LLAMA2
results: []
SFT_FineTuned_LLAMA2
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the MatanP/stories-with_custom_prompts dataset. It achieves the following results on the evaluation set:
- Loss: 1.5881
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9368 | 1.0 | 103 | 1.6480 |
| 1.5178 | 2.0 | 206 | 1.6177 |
| 1.6781 | 3.0 | 309 | 1.5987 |
| 1.4845 | 4.0 | 412 | 1.5906 |
| 1.6539 | 5.0 | 515 | 1.5885 |
| 1.2499 | 6.0 | 618 | 1.5881 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 3.0.0
- Tokenizers 0.19.1