Create README.md

README.md

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+---
+license: apache-2.0
+---
+# NEO
+
+[🤗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)
+
+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](https://huggingface.co/m-a-p/neo_7b)
+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)
+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)
+neo_scalinglaw_980M | This repo contains ckpts related to scalinglaw experiments | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_scalinglaw_980M)
+neo_scalinglaw_460M | This repo contains ckpts related to scalinglaw experiments | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_scalinglaw_460M)
+neo_scalinglaw_250M | This repo contains ckpts related to scalinglaw experiments | • [🤗 Hugging Face](https://huggingface.co/m-a-p/neo_scalinglaw_250M)
+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)
+
+### Usage
+
+```python
+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)
+```
+
+