metadata
language:
- en
tags:
- pytorch
- causal-lm
license: bigscience-openrail-m
GeoV-9B is a 9 billion parameter autoregressive language model.
The GeoV model was designed by Georges Harik and uses Rotary Positional Embeddings with Relative distances (RoPER) by Georges Hark 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 |
|---|---|
| nparameters | 9B |
| nlayers | 32 |
| dmodel | 5120 |
| nheads | 40 |
| dhead | 128 |
| nvocab | 65500 |
| Sequence Length | 2049 |
Generation
The generate() method can be used to generate text using GeoV model.
>>> from transformers 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]