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datedgpt-instruct-* repositories in this organization.| Property | Value |
|---|---|
| Architecture | LlamaForCausalLM |
| Parameters | ~1.3 B |
| Context length | 2048 |
| Vocab | 32,000 (SentencePiece) |
| Precision | bfloat16 |
| Data vintage | 2022 |
1import torch
2from transformers import AutoTokenizer, AutoModelForCausalLM
3
4repo_id = "datedgpt/datedgpt-2022-base"
5tokenizer = AutoTokenizer.from_pretrained(repo_id)
6model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map="auto")
7
8inputs = tokenizer("The stock market in 2022", return_tensors="pt").to(model.device)
9output = model.generate(**inputs, max_new_tokens=64, use_cache=True)
10print(tokenizer.decode(output[0], skip_special_tokens=True))