import torch from transformers import GPTJForCausalLM, AutoTokenizer model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
from transformers import GPTJForCausalLM, AutoTokenizer model_name = "EleutherAI/gpt-j-6B" model = GPTJForCausalLM.from_pretrained( model_name, revision="float16", # Use float16 version for smaller size torch_dtype=torch.float16, low_cpu_mem_usage=True ) download gpt-j
inputs = tokenizer("Hello, I'm", return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0])) import torch from transformers import GPTJForCausalLM
tokenizer = AutoTokenizer.from_pretrained(model_name) low_cpu_mem_usage=True ) inputs = tokenizer("Hello
Here’s a proper write-up for downloading GPT-J, including the recommended method using Hugging Face Transformers. GPT-J-6B is an open‑source autoregressive language model developed by EleutherAI. It has 6 billion parameters and performs competitively with GPT-3 of similar size. Option 1: Download via Hugging Face 🤗 Transformers (Recommended) This method downloads the model automatically when you load it.
