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YuvrajSingh9886/
smol-llama-dpo

Name: smol-llama-dpo
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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:

Pretraining
SFT (Instruction Tuning)
Reward Tuning for human-like responses (DPO)

Test it out here - SmolLlama
Models have been uploaded on HF!

1) Pretraining

Dataset

I used the FineWeb dataset from HuggingFace (10BT checkpoint) consisting of roughly 15M texts.

Train dataset - 12 M texts
Val dataset - 3M texts

2) SFT

Dataset

I used the alpaca dataset from HuggingFace consisting of roughly 52k texts.

Train dataset - 45k texts
Val dataset - 5k texts

3) Reference Alignment

Dataset

I used the Ultrafeedback dataset from HuggingFace consisting of roughly 62.1K texts.

Train dataset - 56K texts
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

Output

Sample Ouput

Sample Ouput 2

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

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 donwload_model_weight.py -D

Running

Training a model

Kindly change'device' to any of your available cuda gpus.

To run:

cd SmolLlama

Prepare the dataset. You can set either "tinystories" or "fw" or "dpo" to True to download the corresponding datasets.

python

python data.py --tinystories

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 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

cd gradio/  

pip install gradio  

python app.py

YuvrajSingh9886/smol-llama-dpo

Introducing SmolLlama - A Smaller Language Model

1) Pretraining

Dataset

2) SFT

Dataset

3) Reference Alignment

Dataset

ModelArgs (Hyperparameters)

Hardware Setup

Frameworks:

Epochs/Steps

Losses

Screenshots of the loss curves

Output

Sample Ouput 2

Local setup

Requirements

Running

Training a model

Inference on a model

YuvrajSingh9886/
smol-llama-dpo