|
--- |
|
license: apache-2.0 |
|
datasets: |
|
- NIEXCHE/turkish_agriculture_QA_llama2_22.6k |
|
language: |
|
- tr |
|
- en |
|
--- |
|
|
|
# Model Card for LLaMA-2-7B-NIEXCHE |
|
|
|
This model was fine-tuned from LLaMA-2-7B on a Turkish agriculture QA dataset. It supports both Turkish and English languages and was trained for use in agriculture-related natural language processing (NLP) tasks. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
- **Developed by:** NIEXCHE (Fevzi KILAS) |
|
- **Finetuned from model:** [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b) |
|
- **License:** Apache-2.0 |
|
- **Language(s) (NLP):** Turkish, English |
|
- **Model type:** LLaMA-2-based model |
|
- **Training Dataset:** [NIEXCHE/turkish_agriculture_QA_llama2_22.6k](https://huggingface.co/datasets/NIEXCHE/turkish_agriculture_QA_llama2_22.6k) |
|
|
|
### Model Sources |
|
|
|
- **Repository:** [Model Repository (TBA)](#) |
|
- **Demo:** [TBA](#) |
|
|
|
## Uses |
|
|
|
### Direct Use |
|
|
|
The model can be used directly for question-answering tasks related to agriculture in Turkish and English. It is fine-tuned specifically for agricultural Q&A, making it suitable for similar domains and use cases. |
|
|
|
### Out-of-Scope Use |
|
|
|
The model might not perform well on general knowledge questions outside of the agriculture domain. |
|
|
|
## Training Details |
|
|
|
### Training Data |
|
|
|
The training data was a custom dataset created by translating and cleaning agricultural QA data from [this source](https://huggingface.co/datasets/KisanVaani/agriculture-qa-english-only). The dataset contains 22.6k question-answer pairs in Turkish. |
|
|
|
### Training Procedure |
|
|
|
The model was trained using the following frameworks and libraries: |
|
- **Frameworks:** PyTorch, `transformers`, `accelerate==0.21.0`, `peft==0.4.0`, `bitsandbytes==0.40.2`, `trl==0.4.7` |
|
- **Precision:** The model was trained using 4-bit quantization (BNB) with mixed precision (`float16`) to optimize memory usage. |
|
|
|
#### Training Hyperparameters |
|
|
|
- **Base Model:** `meta-llama/Llama-2-7b` |
|
- **Batch Size:** 4 (per device) |
|
- **Learning Rate:** 2e-4 |
|
- **LoRA Parameters:** |
|
- lora_r = 64 |
|
- lora_alpha = 16 |
|
- lora_dropout = 0.1 |
|
- **Epochs:** 1 |
|
- **Optimizer:** Paged AdamW (32-bit) |
|
- **Gradient Accumulation Steps:** 1 |
|
- **Scheduler:** Cosine |
|
- **Max Gradient Norm:** 0.3 |
|
- **Gradient Checkpointing:** Enabled |
|
- **Warmup Ratio:** 0.03 |
|
- **Group by Length:** Enabled |
|
- **Max Sequence Length:** None |
|
|
|
### Hardware |
|
|
|
- **Training Hardware:** Google Colab Pro (A100 GPU) and 53 GB system RAM. |
|
- **Training Time:** Approximately 1 hour 40 minutes. |
|
|
|
Training output: |
|
`TrainOutput(global_step=5654, training_loss=0.7829279924898043, metrics={'train_runtime': 6029.996, 'train_samples_per_second': 3.75, 'train_steps_per_second': 0.938, 'total_flos': 5.516196145999872e+16, 'train_loss': 0.7829279924898043, 'epoch': 1.0})` |
|
|
|
|
|
|
|
## Evaluation |
|
|
|
The same dataset (`NIEXCHE/turkish_agriculture_QA_llama2_22.6k`) was used for evaluation purposes. |
|
|
|
## Environmental Impact |
|
|
|
Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). |
|
|
|
- **Hardware Type:** Google Colab (A100 GPU) |
|
- **Hours used:** 1 hour 40 minutes |
|
- **Compute Region:** Google Cloud (Colab) |
|
- **Carbon Emitted:** Estimations pending |
|
|
|
## Citation |
|
|
|
If you use this model in your research or applications, please cite it as: |
|
|
|
```bibtex |
|
@misc{Fevzi2024LLaMA-2-7B-NIEXCHE, |
|
author = {Fevzi KILAS}, |
|
title = {LLaMA-2-7B-NIEXCHE: A Turkish Agriculture QA Model}, |
|
year = {2024}, |
|
howpublished = {https://huggingface.co/NIEXCHE/turkish_agriculture_QA_llama2_22.6k} |
|
} |
|
``` |
|
## Contact: |
|
|
|
[NIEXCHE (Fevzi KILAS)](https://niexche.github.io/) |
|
|