--- library_name: peft license: mit language: - en pipeline_tag: text-generation --- # AlpaGo: GPT-NeoX-20B Model Trained with Qlora Technique AlpaGo is an adapter model trained using the Qlora technique on top of the GPT-NeoX-20B model. This repository contains the code and resources for AlpaGo, which can be used for natural language processing tasks. AlpaGo is built on the [GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b) architecture and developed by Math And AI Institute. ## Features - AlpaGo adapter model trained with the Qlora technique - Based on the GPT-NeoX-20B model, providing high-quality natural language processing capabilities on Engilish Language ## Evaluation - Coming soon - ## Usage You can utilize AlpaGo to perform natural language processing tasks. Here's an example of how to use it: To try via Google Colab:

You can even run it on your own computer if you want. But the warning only works on GPUs with at least 15gb vram. ```python from peft import PeftModel import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GenerationConfig model_id = "EleutherAI/gpt-neox-20b" tokenizer = AutoTokenizer.from_pretrained(model_id) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto") model = PeftModel.from_pretrained(model, "myzens/AlpaGo") #You can change Here. PROMPT = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Write a short story about a lost key that unlocks a mysterious door. ### Response:""" inputs = tokenizer(PROMPT, return_tensors="pt") input_ids = inputs["input_ids"].cuda() generation_config = GenerationConfig( temperature=0.6, top_p=0.95, repetition_penalty=1.15, ) print("Generating...") generation_output = model.generate( input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, ) for s in generation_output.sequences: print(tokenizer.decode(s)) ```