NEO
?Neo-Models | ?Neo-Datasets | Github
Neo is a completely open source large language model, including code, all model weights, datasets used for training, and training details.
Model
| Model | Describe | Download |
|---|---|---|
| neo_7b | This repository contains the base model of neo_7b | • ? Hugging Face |
| neo7bintermediate | This repo contains normal pre-training intermediate ckpts. A total of 3.7T tokens were learned at this phase. | • ? Hugging Face |
| neo7bdecay | This repo contains intermediate ckpts during the decay phase. A total of 720B tokens were learned at this phase. | • ? Hugging Face |
| neoscalinglaw980M | This repo contains ckpts related to scalinglaw experiments | • ? Hugging Face |
| neoscalinglaw460M | This repo contains ckpts related to scalinglaw experiments | • ? Hugging Face |
| neoscalinglaw250M | This repo contains ckpts related to scalinglaw experiments | • ? Hugging Face |
| neo2bgeneral | This repo contains ckpts of 2b model trained using common domain knowledge | • ? Hugging Face |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = '<your-hf-model-path-with-tokenizer>'
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
input_text = "A long, long time ago,"
input_ids = tokenizer(input_text, add_generation_prompt=True, return_tensors='pt').to(model.device)
output_ids = model.generate(**input_ids, max_new_tokens=20)
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(response)
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