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license: apache-2.0 |
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# NEO |
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[🤗Neo-Models](https://huggingface.co/collections/m-a-p/neo-models-66395a5c9662bb58d5d70f04) | [🤗Neo-Datasets](https://huggingface.co/collections/m-a-p/neo-models-66395a5c9662bb58d5d70f04) | [Github](https://github.com/multimodal-art-projection/MAP-NEO) |
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Neo is a completely open source large language model, including code, all model weights, datasets used for training, and training details. |
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## Model |
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| Model | Describe | Download | |
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neo_7b| This repository contains the base model of neo_7b | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b) |
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neo_7b_intermediate| This repo contains normal pre-training intermediate ckpts. A total of 3.7T tokens were learned at this phase. | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b_intermediate) |
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neo_7b_decay| This repo contains intermediate ckpts during the decay phase. A total of 720B tokens were learned at this phase. | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_7b_decay) |
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neo_scalinglaw_980M | This repo contains ckpts related to scalinglaw experiments | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_scalinglaw_980M) |
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neo_scalinglaw_460M | This repo contains ckpts related to scalinglaw experiments | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_scalinglaw_460M) |
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neo_scalinglaw_250M | This repo contains ckpts related to scalinglaw experiments | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_scalinglaw_250M) |
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neo_2b_general | This repo contains ckpts of 2b model trained using common domain knowledge | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_2b_general) |
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### Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_path = '<your-hf-model-path-with-tokenizer>' |
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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device_map="auto", |
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torch_dtype='auto' |
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).eval() |
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input_text = "A long, long time ago," |
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input_ids = tokenizer(input_text, add_generation_prompt=True, return_tensors='pt').to(model.device) |
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output_ids = model.generate(**input_ids, max_new_tokens=20) |
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response = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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print(response) |
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``` |
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