berkecanrizai commented 1 year ago

Hi, I want to fine-tune the 7b model, am I supposed to download the provided checkpoint and fine-tune it as shown in this repo: https://github.com/EleutherAI/gpt-neox#using-custom-data . Would they be compatible and did anyone here give it a shot? Thanks.

Ph0rk0z commented 1 year ago

It looks like it needs much more training before any fine-tuning you do will be worth it.

devonkinghorn commented 1 year ago

What about using the databricks-dolly-15k dataset? https://github.com/databrickslabs/dolly/tree/master/data

Ph0rk0z commented 1 year ago

from: https://docs.google.com/spreadsheets/d/1kT4or6b0Fedd-W_jMwYpb63e1ZR3aePczz3zlbJW-Y4/edit#gid=0

Model | RAM | lambada(ppl) | lambada(acc) | hellaswag(acc_norm) | winogrande(acc) | piqa(acc) | coqa(f1) | average -- | -- | -- | -- | -- | -- | -- | -- | -- stablelm-base-alpha-7b | 32 | 17.6493 | 41.06% | 41.22% | 50.12% | 66.76% | | 49.79% -- | -- | -- | -- | -- | -- | -- | -- | -- Look at the perplexity scores of this model right now. It is worse than a 500m. Wait till they finish it. You can thumbs down me 100x but it won't fix it.

samuelazran commented 1 year ago

@Ph0rk0z any idea whats the plan for release date of further checkpoints? I think training it on more than 1 trillion tokens can give it advantage compare to other pre-trained models.

Ph0rk0z commented 1 year ago

I wish.. I don't work for them. Hopefully they finish training and get rid of the disclaimers. Then this will be a great model for long contexts.. the first I found besides RWKV.

snirbenyosef commented 1 year ago

I'm also interested in fine-tuning the model on my book, is it possible? did someone have an idea how to start? I tried with transformers but the result was not that good. I just gave it some text. but i will need to process that text i guess.

juanps90 commented 1 year ago

I'm interested in finetuning as well. Does anyone have any recommendation for this?

Ph0rk0z commented 1 year ago

https://github.com/oobabooga/text-generation-webui/blob/main/docs/Using-LoRAs.md

https://github.com/johnsmith0031/alpaca_lora_4bit

If you want to try to make a lora.

mcmonkey4eva commented 1 year ago

You can fine-tune now with the links Ph0rk0z posted above, but ... yeah wait for the next release, the Alpha's are just that - initial Alpha's not meant for real usage, just meant to be open public development.

aamir-gmail commented 1 year ago

Hi, I want to fine-tune the 7b model, am I supposed to download the provided checkpoint and fine-tune it as shown in this repo: https://github.com/EleutherAI/gpt-neox#using-custom-data . Would they be compatible and did anyone here give it a shot? Thanks.

I have a training script for 7B and 3B , where can I send it,

snirbenyosef commented 1 year ago

Hi, I want to fine-tune the 7b model, am I supposed to download the provided checkpoint and fine-tune it as shown in this repo: https://github.com/EleutherAI/gpt-neox#using-custom-data . Would they be compatible and did anyone here give it a shot? Thanks.

I have a training script for 7B and 3B , where can I send it,

@aamir-gmail can you send it to me snirben@gmail.com please?

vonbarnekowa commented 1 year ago

@aamir-gmail, would be cool if you can share it here.

Shaila96 commented 1 year ago

@aamir-gmail could you please send it to me also? shaila.zaman96@gmail.com

aamir-gmail commented 1 year ago

Here you go the full training script `# Developed by Aamir Mirza

create a conda virtual environment python 3.9

install PyTorch 1.13.1 ( not 2.0)

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

install the latest transformers

conda install -c conda-forge transformers

install deepspeed from GitHub not pip install

build deepspeed with CPU Adam optimiser support like this

git clone https://github.com/microsoft/DeepSpeed

DS_BUILD_CPU_ADAM=1 pip install .

accelerate via pip

pip install Ninja

conda install -c conda-forge mpi4py

train via commandline for example

deepspeed train_gptNX_v2.py --num_gpus=2

In my case I have 2x 3090 24GB

from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast, TextDataset, \ DefaultDataCollator, DataCollatorForLanguageModeling, DataCollatorWithPadding from transformers import Trainer, TrainingArguments from datasets import load_dataset import os

os.environ['OMPI_MCA_opal_cuda_support'] = 'true' os.environ['TOKENIZERS_PARALLELISM'] = 'false'

If you got a single GPU then change this to one

os.environ["WORLD_SIZE"] = "2"

Change this to your requirement for example 4096 (MAX)

MAX_LEN = 1024

stage2_config = """{ "bf16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 },

"optimizer": {
    "type": "AdamW",
    "params": {
        "lr": "auto",
        "betas": "auto",
        "eps": "auto",
        "weight_decay": "auto"
    }
},

"scheduler": {
    "type": "WarmupLR",
    "params": {
        "warmup_min_lr": "auto",
        "warmup_max_lr": "auto",
        "warmup_num_steps": "auto"
    }
},

"zero_optimization": {
    "stage": 2,
    "offload_optimizer": {
        "device": "cpu",
        "pin_memory": true
    },
    "allgather_partitions": true,
    "allgather_bucket_size": 2e8,
    "overlap_comm": true,
    "reduce_scatter": true,
    "reduce_bucket_size": 2e8,
    "contiguous_gradients": true
},

"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto"

} """

class CustomTrainer(Trainer): def compute_loss(self, model_a, inputs_a, return_outputs=False): strd = ' ' outputs = model_a(**inputs_a, labels=inputs_a["input_ids"]) loss = outputs.loss return (loss, outputs) if return_outputs else loss

tokenizer = GPTNeoXTokenizerFast.from_pretrained("stabilityai/stablelm-base-alpha-3b")

def process_data(examples): texts = examples["text"]

Remove empty lines

texts = [text for text in texts if len(text) > 0 and not text.isspace()]
# Remove lines that are too long
texts = [text for text in texts if len(text) < 512]
# Remove lines that are too short
texts = [text for text in texts if len(text) > 16]
# add newline character
texts = [text + ' ' + '\n' for text in texts]
examples["text"] = texts
return examples

process dataset columns [text] use tokenizer to get input_ids and attention mask

def process_data_add_mask(examples): text = examples['text'] tokenizer.pad_token = tokenizer.eos_token

Tokenize text

encoded_dict = tokenizer(
    text,
    padding=True,
    truncation=True,
    max_length=MAX_LEN
)
# Add input_ids and attention_mask to example
examples['input_ids'] = encoded_dict['input_ids']
examples['attention_mask'] = encoded_dict['attention_mask']
return examples

imdb_dataset = load_dataset('imdb') imdb_dataset_train = imdb_dataset['train'] imdb_dataset_train = imdb_dataset_train.shuffle() imdb_dataset_train = imdb_dataset_train.map(process_data, batched=True, remove_columns=['label']) imdb_dataset_val = imdb_dataset['test'] imdb_dataset_val = imdb_dataset_val.shuffle() imdb_dataset_val = imdb_dataset_val.map(process_data, batched=True, remove_columns=['label']) train_dataset = imdb_dataset_train.map(process_data_add_mask, remove_columns=["text"], batched=True) val_dataset = imdb_dataset_val.map(process_data_add_mask, remove_columns=["text"], batched=True) strs = " "

model = GPTNeoXForCausalLM.from_pretrained("stabilityai/stablelm-base-alpha-3b")

absolute path required for deepspeed config

you can use the JSON above to create your own config

z_optimiser = '/two-tb/train_GPTNX/zeromq_config/stablelm-base-alpha-3b_config.json' data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt") training_args_v2 = TrainingArguments( output_dir="./trained_model", learning_rate=2e-5, save_total_limit=2, fp16=True, per_device_train_batch_size=1, per_device_eval_batch_size=12, evaluation_strategy="epoch", deepspeed=z_optimiser, num_train_epochs=1 )

Set up the trainer

trainer = CustomTrainer( model=model, args=training_args_v2, train_dataset=train_dataset, eval_dataset=val_dataset, data_collator=data_collator, tokenizer=tokenizer, ) trainer.train()

trainer.save_model()

`

aamir-gmail commented 1 year ago

I have shard the script works for 3B as well as 7 B

On Wed, May 3, 2023 at 2:21 AM Shaila Zaman @.***> wrote:

@aamir-gmail https://github.com/aamir-gmail could you please send it to me also? @.***

— Reply to this email directly, view it on GitHub https://github.com/Stability-AI/StableLM/issues/37#issuecomment-1531760092, or unsubscribe https://github.com/notifications/unsubscribe-auth/AJA2ECO7YGIAWLBC245OMZDXEEYBFANCNFSM6AAAAAAXGAX72I . You are receiving this because you were mentioned.Message ID: @.***>

-- Kind Regards

Aamir Mirza

Appleyc commented 1 year ago

I've been too busy recently, I just saw this message now, thank you very much！

在 2023-05-03 11:19:14，"Aamir Mirza" @.***> 写道：

Here you go the full training script `# Developed by Aamir Mirza

create a conda virtual environment python 3.9 install PyTorch 1.13.1 ( not 2.0) conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia install the latest transformers conda install -c conda-forge transformers install deepspeed from GitHub not pip install build deepspeed with CPU Adam optimiser support like this git clone https://github.com/microsoft/DeepSpeed DS_BUILD_CPU_ADAM=1 pip install . accelerate via pip pip install Ninja conda install -c conda-forge mpi4py train via commandline for example deepspeed train_gptNX_v2.py --num_gpus=2 In my case I have 2x 3090 24GB

from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast, TextDataset, DefaultDataCollator, DataCollatorForLanguageModeling, DataCollatorWithPadding from transformers import Trainer, TrainingArguments from datasets import load_dataset import os

os.environ['OMPI_MCA_opal_cuda_support'] = 'true' os.environ['TOKENIZERS_PARALLELISM'] = 'false'

If you got a single GPU then change this to one

os.environ["WORLD_SIZE"] = "2"

Change this to your requirement for example 4096 (MAX)

MAX_LEN = 1024

stage2_config = """{ "bf16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 },

"optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } },

"scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } },

"zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 2e8, "contiguous_gradients": true },

"gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto"

} """

class CustomTrainer(Trainer): def compute_loss(self, model_a, inputs_a, return_outputs=False): strd = ' ' outputs = model_a(**inputs_a, labels=inputs_a["input_ids"]) loss = outputs.loss return (loss, outputs) if return_outputs else loss

tokenizer = GPTNeoXTokenizerFast.from_pretrained("stabilityai/stablelm-base-alpha-3b")

def process_data(examples): texts = examples["text"]

Remove empty lines

texts = [text for text in texts if len(text) > 0 and not text.isspace()]

Remove lines that are too long

texts = [text for text in texts if len(text) < 512]

Remove lines that are too short

texts = [text for text in texts if len(text) > 16]

add newline character

texts = [text + ' ' + '\n' for text in texts] examples["text"] = texts return examples

process dataset columns [text] use tokenizer to get input_ids and attention mask

def process_data_add_mask(examples): text = examples['text'] tokenizer.pad_token = tokenizer.eos_token

Tokenize text

encoded_dict = tokenizer( text, padding=True, truncation=True, max_length=MAX_LEN )

Add input_ids and attention_mask to example

examples['input_ids'] = encoded_dict['input_ids'] examples['attention_mask'] = encoded_dict['attention_mask'] return examples

imdb_dataset = load_dataset('imdb') imdb_dataset_train = imdb_dataset['train'] imdb_dataset_train = imdb_dataset_train.shuffle() imdb_dataset_train = imdb_dataset_train.map(process_data, batched=True, remove_columns=['label']) imdb_dataset_val = imdb_dataset['test'] imdb_dataset_val = imdb_dataset_val.shuffle() imdb_dataset_val = imdb_dataset_val.map(process_data, batched=True, remove_columns=['label']) train_dataset = imdb_dataset_train.map(process_data_add_mask, remove_columns=["text"], batched=True) val_dataset = imdb_dataset_val.map(process_data_add_mask, remove_columns=["text"], batched=True) strs = " "

model = GPTNeoXForCausalLM.from_pretrained("stabilityai/stablelm-base-alpha-3b")

absolute path required for deepspeed config you can use the JSON above to create your own config

z_optimiser = '/two-tb/train_GPTNX/zeromq_config/stablelm-base-alpha-3b_config.json' data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt") training_args_v2 = TrainingArguments( output_dir="./trained_model", learning_rate=2e-5, save_total_limit=2, fp16=True, per_device_train_batch_size=1, per_device_eval_batch_size=12, evaluation_strategy="epoch", deepspeed=z_optimiser, num_train_epochs=1 )

Set up the trainer

trainer = CustomTrainer( model=model, args=training_args_v2, train_dataset=train_dataset, eval_dataset=val_dataset, data_collator=data_collator, tokenizer=tokenizer, ) trainer.train()

trainer.save_model()

`

— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: @.***>

aamir-gmail commented 1 year ago

Same works for 3B and 7B

On Mon, May 22, 2023 at 2:08 PM xuantoan02 @.***> wrote:

Hi, I want to fine-tune the 7b model, am I supposed to download the provided checkpoint and fine-tune it as shown in this repo: https://github.com/EleutherAI/gpt-neox#using-custom-data . Would they be compatible and did anyone here give it a shot? Thanks.

I have a training script for 7B and 3B , where can I send it,

could you please send it to me .. @.*** thank you !

— Reply to this email directly, view it on GitHub https://github.com/Stability-AI/StableLM/issues/37#issuecomment-1556494251, or unsubscribe https://github.com/notifications/unsubscribe-auth/AJA2ECME7PICQHZ5RCSL6SLXHLRFTANCNFSM6AAAAAAXGAX72I . You are receiving this because you were mentioned.Message ID: @.***>

-- Kind Regards

Aamir Mirza

Stability-AI / StableLM

How to Fine-tune the Model? #37

create a conda virtual environment python 3.9

install PyTorch 1.13.1 ( not 2.0)

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

install the latest transformers

conda install -c conda-forge transformers

install deepspeed from GitHub not pip install

build deepspeed with CPU Adam optimiser support like this

git clone https://github.com/microsoft/DeepSpeed

DS_BUILD_CPU_ADAM=1 pip install .

accelerate via pip

pip install Ninja

conda install -c conda-forge mpi4py

train via commandline for example

deepspeed train_gptNX_v2.py --num_gpus=2

In my case I have 2x 3090 24GB

If you got a single GPU then change this to one

Change this to your requirement for example 4096 (MAX)

Remove empty lines

process dataset columns [text] use tokenizer to get input_ids and attention mask

Tokenize text

absolute path required for deepspeed config

you can use the JSON above to create your own config

Set up the trainer

trainer.save_model()

Remove empty lines

Remove lines that are too long

Remove lines that are too short

add newline character

Tokenize text

Add input_ids and attention_mask to example