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Model: datedgpt/datedgpt-2016-base
Source: Original Platform

README.md

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+---
+language:
+- en
+tags:
+- causal-lm
+- llama
+- point-in-time
+- dated
+- lookahead-bias-free
+pipeline_tag: text-generation
+---
+
+# DatedGPT-2016 (base)
+
+**DatedGPT** is a family of point-in-time language models: each vintage is
+trained only on data available up to its cutoff date, making it suitable for
+lookahead-bias-free prediction and point-in-time analysis.
+
+This is the **base (pretrained) model** with data up to **2016** — no
+instruction tuning. For the instruction-tuned variants, see the
+`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 | 2016 |
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+repo_id = "datedgpt/datedgpt-2016-base"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+inputs = tokenizer("The stock market in 2016", return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=64, use_cache=True)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- Base model: completions only, no chat/instruction following.
+- Knowledge limited to the 2016 data vintage.
+- No RLHF or safety tuning.