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.

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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,

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

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@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.

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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.

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

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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 [email protected] please?

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

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@aamir-gmail could you please send it to me also? [email protected]

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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()

`

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