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GeoV/
GeoV-9b

Name: GeoV-9b
Availability: InStock

bigscience-openrail-m

autotrain_compatible

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GeoV-9B is a 9 billion parameter causal language model.

The GeoV model was designed by Georges Harik and uses Rotary Positional Embeddings with Relative distances (RoPER) by Georges Harik and Varuna Jayasiri.

RoPER, in addition to using relative positions in the attention score calculation by RoPE embeddings, adds relative positional information explicitly to value embeddings. Specifically, it incorporates the relative positions of the tokens paid attention to. RoPER has given better performance in some algorithmic tasks, and seems comparable to RoPE in language modeling.

Model details

Developed by: Georges Harik
Model type: Transformer-based Language Model
Language: English

Hyperparameter	Value
n_parameters	9B
n_layers	32
d_model	5120
n_heads	40
d_head	128
n_vocab	65500
Sequence Length	2048

The released weights were trained on ~70 billion tokens. We plan to continue training up to 300 billion tokens and update the weights at every 20b tokens. This training run is monolingual and uses c4en and english wikipedia datasets.

Test results

These are the results from EleutherAI/lm-evaluation-harness at 80B (tokens trained) checkpoint.

Task	Version	Metric	Value		Stderr
anli_r1	0	acc	0.3150	±	0.0147
anli_r2	0	acc	0.3380	±	0.0150
anli_r3	0	acc	0.3367	±	0.0136
hellaswag	0	acc	0.4761	±	0.0050
		acc_norm	0.6308	±	0.0048
lambada_openai	0	ppl	8.9700	±	0.2606
		acc	0.5628	±	0.0069
mathqa	0	acc	0.2318	±	0.0077
		acc_norm	0.2372	±	0.0078
piqa	0	acc	0.7448	±	0.0102
		acc_norm	0.7639	±	0.0099
winogrande	0	acc	0.5935	±	0.0138
wsc	0	acc	0.4038	±	0.0483

Installation

pip install geov

Generation

Open In Colab

python

from geov import GeoVForCausalLM, GeoVTokenizer

model = GeoVForCausalLM.from_pretrained("GeoV/GeoV-9b")
tokenizer = GeoVTokenizer.from_pretrained("GeoV/GeoV-9b")

prompt = "In mathematics, topology is the study of"

input_ids = tokenizer(prompt, return_tensors="pt").input_ids

gen_tokens = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.9,
    max_length=100,
)
gen_text = tokenizer.batch_decode(gen_tokens)[0]