README.md

---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
- Pytorch
- Falcon
base_model: tiiuae/falcon-7b
model-index:
- name: saqr-7b-instruct
  results: []
datasets:
- HuggingFaceH4/ultrachat_200k
- openbmb/UltraFeedback
- gsm8k
language:
- en
pipeline_tag: text-generation
---

<img src="https://huggingface.co/Menouar/saqr-7b-instruct/resolve/main/saqr.jpg" alt="Saqr Logo" width="800" style="margin-left:auto; margin-right:auto; display:block;"/>


# saqr-7b-instruct

This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k), [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback), and [gsm8k](https://huggingface.co/datasets/gsm8k)  datasets.

## Model description

This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) using supervised fine-tuning on nearly the same datasets as Zephyr-7B-beta.

## Training and evaluation data

The evaluation for training can be found [here](https://huggingface.co/Menouar/saqr-7b-instruct/tensorboard).

The evaluation can be found at the Hugging Face Leaderboard [here](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Menouar/saqr-7b-instruct/).


## Training procedure

Can be found [here](https://colab.research.google.com/github/menouarazib/llm/blob/main/Saqr_7B.ipynb).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 7
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 5000

### Training results



### Framework versions

- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1