metadata

library_name: transformers
license: apache-2.0
datasets:
  - pszemraj/flan-subsets-deduped
language:
  - en
base_model: pszemraj/tFINE-900m-e16-d32-1024ctx
pipeline_tag: text2text-generation

BEE-spoke-data/tFINE-900m-e16-d32-flan

This is a basic text-to-text "instruct" model, similar to Google's original flan-t5 model series (but not trained for as long).

Details: Click here to expand

Fine-tuned from the base model on the pszemraj/flan-subsets-deduped dataset, subset flan-v2 for 1 epoch. It achieves the following results on the evaluation set:

Loss: 1.4134
Rouge1: 62.9142
Rouge2: 22.5279
Rougel: 61.4902
Rougelsum: 61.7795
Gen Len: 12.0586
Num Input Tokens Seen: 1931815668

Model features

pretrained & fine-tuned at 1024 context length (input)
tokenizer with byte-pair fallback to support understanding and generating text beyond what the original T5 tokenizer does

Usage Example

from transformers import pipeline

pipe = pipeline(
    "text2text-generation",
    model="BEE-spoke-data/tFINE-900m-e16-d32-flan",
)
prompt = "What color is tuesday?"
res = pipe(prompt, max_new_tokens=96, top_k=4, penalty_alpha=0.6)
print(res[0]["generated_text"])

Quick eval

Quick eval for: BEE-spoke-data/tFINE-900m-e16-d32-flan

hf (pretrained=BEE-spoke-data/tFINE-900m-e16-d32-flan,trust_remote_code=True,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
boolq	2	none	0	acc	↑	0.6700	±	0.0082
openbookqa	1	none	0	acc	↑	0.1900	±	0.0176
		none	0	acc_norm	↑	0.2980	±	0.0205
piqa	1	none	0	acc	↑	0.6001	±	0.0114
		none	0	acc_norm	↑	0.6072	±	0.0114
social_iqa	0	none	0	acc	↑	0.4299	±	0.0112
tinyArc	0	none	25	acc_norm	↑	0.3214	±	N/A
tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.0492	±	N/A
		strict-match	5	exact_match	↑	0.0380	±	N/A
tinyHellaswag	0	none	10	acc_norm	↑	0.4005	±	N/A
tinyMMLU	0	none	0	acc_norm	↑	0.2857	±	N/A
winogrande	1	none	0	acc	↑	0.4988	±	0.0141