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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - causal-lm
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+ - llama
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+ - point-in-time
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+ - dated
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+ - lookahead-bias-free
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # DatedGPT-2013 (base)
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+
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+ **DatedGPT** is a family of point-in-time language models: each vintage is
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+ trained only on data available up to its cutoff date, making it suitable for
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+ lookahead-bias-free prediction and point-in-time analysis.
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+
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+ This is the **base (pretrained) model** with data up to **2013** — no
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+ instruction tuning. For the instruction-tuned variants, see the
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+ `datedgpt-instruct-*` repositories in this organization.
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+
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+ | Property | Value |
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+ |----------|-------|
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+ | Architecture | LlamaForCausalLM |
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+ | Parameters | ~1.3 B |
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+ | Context length | 2048 |
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+ | Vocab | 32,000 (SentencePiece) |
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+ | Precision | bfloat16 |
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+ | Data vintage | 2013 |
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+
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+ ## Usage
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+
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+ ```python
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+ import torch
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ repo_id = "datedgpt/datedgpt-2013-base"
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+ tokenizer = AutoTokenizer.from_pretrained(repo_id)
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+ model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map="auto")
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+
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+ inputs = tokenizer("The stock market in 2013", return_tensors="pt").to(model.device)
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+ output = model.generate(**inputs, max_new_tokens=64, use_cache=True)
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+ print(tokenizer.decode(output[0], skip_special_tokens=True))
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+ ```
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+
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+ ## Limitations
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+
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+ - Base model: completions only, no chat/instruction following.
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+ - Knowledge limited to the 2013 data vintage.
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+ - No RLHF or safety tuning.
config.json ADDED
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "hidden_act": "silu",
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+ "initializer_range": 0.02,
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+ "intermediate_size": 5504,
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+ "mlp_bias": false,
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+ "model_type": "llama",
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+ "num_hidden_layers": 24,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.52.4",
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+ "vocab_size": 32000
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+ }
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