TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M

TimeCapsuleLLM — English 1800–1875 (v3 mini eval, 500M)

Model Overview

v3 mini-eval model, trained from scratch on a ~20GB (5B-token) sample of 1800–1875 English texts using a Llama-based Causal Language Model.

Configuration Details

This model is a custom size and configuration based on Llama:

Training data

• Corpus: english-historical-corpus-1800-1875-15GB-sample (sample_5b_tokens)
• 863,087 documents, ~5.71B tokens, packed into 1,393,977 sequences of length 4096

Training

• From scratch, 13,000 steps (~0.9 epoch), effective batch 96, LR 3e-4 (500 warmup, linear decay), AdamW fused, bf16+tf32, FlashAttention-2
• 1x H100 SXM 80GB, ~21h, final train loss ~2.9

Evaluation notes

• Strong, consistent period style across registers (newspaper, sermon, diary, parliamentary, scientific).
• No date leakage surfaced; date-seeded prompts stayed within 1800–1875.
• OCR residue is the main corpus cleanup item: end-of-line hyphenation (ap-\npear) and character-confusion errors (deatli, lie for "he").
• Weak on procedural/recipe text; topic drift over long generations (expected at this scale).

Usage

Model weights and tokenizer are in the final/ subfolder.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
REPO = "haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3-mini-eval"
tok = AutoTokenizer.from_pretrained(REPO, subfolder="final")
model = AutoModelForCausalLM.from_pretrained(REPO, subfolder="final", dtype=torch.bfloat16).eval()

Cost

This model was trained on an H100 SXM from RunPod

Total: $85