model = AutoModelForCausalLM.from_pretrained(..., device_map="auto", max_memory=0: "10GiB", "cpu": "30GiB")
GPT-J boasts several impressive features that make it an attractive option for AI enthusiasts, researchers, and developers:
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]))
You forgot to load the tokenizer correctly. Always use AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B") , not a generic tokenizer. model = AutoModelForCausalLM
, GPT-J is smaller than the largest GPT-3 models but punches far above its weight. It is often cited for its efficiency and specialized performance: Architecture : It uses a parallel decoder architecture
ls -lh
After download, test that the model loads correctly:
If you have successfully downloaded GPT-J, you are ready to explore more powerful open-source models: It is often cited for its efficiency and