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---
title: "SmolLlama"
emoji: "🦙"
colorFrom: "blue"
colorTo: "purple"
sdk: "gradio"
sdk_version: "3.1.0"
app_file: "app.py"
pinned: false
---
# Introducing SmolLlama - A Smaller Language Model
- So, I trained a Llama a 130M architecture I coded from ground up to build a small instruct model, going through the below-mentioned stages from scratch.
- Trained on FineWeb dataset form HuggingFace consisting of 15 M texts (10BT snapshot) for a total of full 3 epochs
The three main stages are:
1) Pretraining
2) SFT (Instruction Tuning)
3) Reward Tuning for human-like responses (DPO)
- Test it out here - [SmolLlama](https://huggingface.co/spaces/YuvrajSingh9886/SmolLlama)
- Models have been uploaded on HF!
### 1) Pretraining
#### Dataset
- I used the [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb?row=0) dataset from HuggingFace (10BT checkpoint) consisting of roughly 15M texts.
1) Train dataset - 12 M texts
2) Val dataset - 3M texts
### 2) SFT
#### Dataset
- I used the [alpaca](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset from HuggingFace consisting of roughly 52k texts.
1) Train dataset - 45k texts
2) Val dataset - 5k texts
### 3) Reference Alignment
#### Dataset
- I used the [Ultrafeedback](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) dataset from HuggingFace consisting of roughly 62.1K texts.
1) Train dataset - 56K texts
2) Val dataset - 6K texts
---
#### ModelArgs (Hyperparameters)
| Parameter | Description | Default Value | Type |
|--------------------------------|-----------------------------------------------------------------------------|-----------------------------------|-----------|
| `epochs` | Number of training epochs | `4` | `int` |
| `block_size` | Size of each block (context length) | `512` | `int` |
| `batch_size` | Batch size for training | `64` | `int` |
| `inference` | Inference mode (not specified) | `None` | `None` |
| `embeddings_dims` | Dimensionality of embeddings | `512` | `int` |
| `attn_dropout` | Dropout rate for attention layers | `0.1` | `float` |
| `no_of_heads` | Number of attention heads | `8` | `int` |
| `dropout` | Dropout rate for the model | `0.1` | `float` |
| `val_epochs` | Number of validation epochs | `2` | `int` |
| `max_lr` | Maximum learning rate | `6e-4` | `float` |
| `no_of_decoder_layers` | Number of decoder layers | `8` | `int` |
| `weight_decay_optim` | Weight decay for the optimizer | `0.1` | `float` |
| `beta_1` | Beta 1 for Adam optimizer | `0.9` | `float` |
| `beta_2` | Beta 2 for Adam optimizer | `0.95` | `float` |
| `clip` | Gradient clipping value | `1.0` | `float` |
| `device` | Device to run the model (`cuda` or `cpu`) | `'cuda'` | `str` |
| `no_kv_heads` | Number of key-value heads | `2` | `int` |
| `vocab_size` | Size of the vocabulary | `50304` | `int` |
| `eps` | Epsilon value for numerical stability | `1e-5` | `float` |
| `dtype` | Data type for tensors (`bfloat16` if supported, else `float16`) | `'bfloat16'` or `'float16'` | `str` |
| `save_checkpoint_dir` | Directory to save model checkpoints | `"checkpoints"` | `str` |
| `prompt` | Default prompt for inference | `"Once upon a time"` | `str` |
| `save_checkpoint_iter` | Save checkpoint every N iterations | `50` | `int` |
| `total_iters` | Total number of training iterations | `10000` | `int` |
| `eval_iters` | Evaluate model every N iterations | `50` | `int` |
| `eval_check` | Check evaluation metrics every N iterations | `100` | `int` |
| `warmup_iters` | Number of warmup iterations for learning rate scheduling | `700` | `int` |
| `min_lr` | Minimum learning rate (10% of `max_lr`) | `0.1 * max_lr` | `float` |
| `lr_decay_iters` | Number of iterations for learning rate decay | `10000` | `int` |
| `total_batch_size` | Total batch size across all devices | `524288` | `int` |
| `micro_batch_size` | Micro batch size per device | `batch_size` | `int` |
| `gradient_accumulation_steps` | Gradient accumulation steps | `total_batch_size // (micro_batch_size * (block_size * torch.cuda.device_count()))` | `int` |
| `no_kv_heads` | Number of key-value heads | `2` | `int` |
---
### Hardware Setup
- Used DPP using Pytorch torchrun consisting of 2x H100s SXM (80GB VRAM each) rented on runpod.io
- The model is a 1.1GB in size but needs around 1.1 GB of VRAM when loaded in fp32 precision
---
#### Frameworks:
**Pytorch**
---
#### Epochs/Steps
- Iterations (train) = 6K (gradient accumulation of 0.5M tokens)
- Val iterations = every 50 steps
---
#### Losses
- Result - Pretraining
Train loss: 3.77 (stagnated)
Val Loss: 3.80 (stagnated)
- Result - SFT
Train Loss: 1.01
Val Loss: 1.39
- Result - DPO
Train Loss: 0.96
Val Loss: 1.05
---
#### Screenshots of the loss curves
- Pretrain
- SFT
- DPO
---
#### Output
- Sample Ouput
---
### Local setup
### Requirements
```python
git [clone the repo](https://github.com/YuvrajSingh-mist/SmolLlama.git)
cd SmolLlama
bash ./install.sh
```
- A wandb.ai account for plotting graphs for your loss curves
- On your terminal run
```python
wandb login
```
- Enter the api key and follow the instructions and once you are succesfully logged in follow the given steps
- Download the model
Can use 'P or F or D' to download the model, stands for pretarined, fine tuned and preference alignment models resp.
```python
python donwload_model_weight.py -D
```
---
### Running
#### Training a model
- Kindly hange 'device' to any of your available cuda gpus.
To run:
```python
cd SmolLlama
```
Prepare the dataset. You can set either "tinystories" or "fw" or "dpo" to True to downalod the corresponding datasets.
```python
python data.py --tinystories
```
```python
bash ./install.sh
```
Download the weights of the model
```python
python download_model_weight.py -sft/dpo/pretrained
```
Train the model
Now run the following command (Use 'P' for pretraining/SFT and 'D' for DPO)
```python
torchrun --standalone --nproc_per_node=gpu trainer.py \
--train P \
--epochs 4 \
--beta 0.1 \
--block_size 256 \
--batch_size 128 \
--embeddings_dims 512 \
--attn_dropout 0.1 \
--no_of_heads 8 \
--dropout 0.1 \
--val_epochs 2 \
--max_lr 6e-4 \
--no_of_decoder_layers 16 \
--weight_decay_optim 0.1 \
--beta_1 0.9 \
--beta_2 0.95 \
--clip 1.0 \
--device cuda \
--no_kv_heads 2 \
--vocab_size 50304 \
--eps 1e-5 \
--dtype "bfloat16" \
--save_checkpoint_dir "checkpoints" \
--prompt "Once upon a time" \
--save_checkpoint_iter 50 \
--total_iters 20000 \
--eval_iters 50 \
--eval_check 100 \
--warmup_iters 700 \
--min_lr 6e-5 \
--lr_decay_iters 20000 \
--total_batch_size 524288 \
--micro_batch_size 128 \
--gradient_accumulation_steps 4096
--max_length 100 \
--temperature 0.8
```
#### Inference on a model
- First method:
```python
python inference_sft.py --prompt "Follow the given instruction carefully. What was the Civil Rights Movement?" --max_length 256 --temperature 0.8
```
- Second method (through Huggingface):
<!-- python download_model_weight.py -sft/dpo/pretrained -->
```python
cd gradio/
pip install gradio
python app.py
```
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