metadata
license: apache-2.0
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 |
| neo_7b_sft_v0.1 | This repository contains the supervised fine-tuning version of the neo_7b model. | • 🤗 Hugging Face |
| neo_7b_instruct_v0.1 | This repository contains the instruction-tuned version of the neo_7b model. | • 🤗 Hugging Face |
| neo_7b_intermediate | This repo contains normal pre-training intermediate ckpts. A total of 3.7T tokens were learned at this phase. | • 🤗 Hugging Face |
| neo_7b_decay | This repo contains intermediate ckpts during the decay phase. A total of 720B tokens were learned at this phase. | • 🤗 Hugging Face |
| neo_scalinglaw_980M | This repo contains ckpts related to scalinglaw experiments | • 🤗 Hugging Face |
| neo_scalinglaw_460M | This repo contains ckpts related to scalinglaw experiments | • 🤗 Hugging Face |
| neo_scalinglaw_250M | This repo contains ckpts related to scalinglaw experiments | • 🤗 Hugging Face |
| neo_2b_general | 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()
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello, what can you help me do?"},
]
input_ids = tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'), max_new_tokens=20)
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
print(response)