tFINE-680m-e32-d16-gqa-flan

tFINE-680m-e32-d16-gqa-flan

FLAN-tuned variant of a tFINE (t5) model with GQA.

• 32 encoder layers
• 16 decoder layers
• 1024 hidden size

testing

install transformers fork with GQA updates for t5 (⚠️WIP🚧):

pip install -U git+https://github.com/pszemraj/transformers.git@t5-gqa

then

# pip install -U git+https://github.com/pszemraj/transformers.git@t5-gqa
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/tFINE-680m-e32-d16-gqa-flan")
model = AutoModelForSeq2SeqLM.from_pretrained(
    "BEE-spoke-data/tFINE-680m-e32-d16-gqa-flan"
)

prompt = "What is the capital of France?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=64, no_repeat_ngram_size=3)
print(
    tokenizer.batch_decode(
        generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
    )[0]
)

Quick eval

Quick eval for: BEE-spoke-data/tFINE-680m-e32-d16-gqa-flan

hf (pretrained=BEE-spoke-data/tFINE-680m-e32-d16-gqa-flan,trust_remote_code=True,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8

Training and evaluation data

used config 'all'

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 8e-05
• train_batch_size: 4
• eval_batch_size: 2
• seed: 17868
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 32
• total_train_batch_size: 256
• total_eval_batch_size: 4
• optimizer: Use paged_ademamix_32bit and the args are: No additional optimizer arguments
• lr_scheduler_type: constant_with_warmup
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 1.0