Instructions to use haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M

SGLang

How to use haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M with Docker Model Runner:
```
docker model run hf.co/haykgrigorian/TimeCapsuleLLM-English-1800-1875-v3mini-eval1-500M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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.

Detail	Value
Model Architecture	LlamaForCausalLM (Decoder-Only Transformer)
Parameter Count	~500M
Training Type	Trained from Scratch (Random Initialization)
Tokenizer	Custom BPE, Vocab Size 32,000
Sequence Length	4096 tokens
Attention Type	Grouped Query Attention (GQA)

Configuration Details

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

Parameter	Value
Number of Layers	24
Hidden Size (d)	1280
Intermediate Size ($\text{d}_{\text{ff}}$)	3456
Attention Heads	20 (Query) / 5 (Key/Value)
Activation Function	SiLU (`silu`)
Normalization	RMS Norm (`rms_norm_eps`: 1e-06)
Position Embeddings	Rotary Positional Embeddings (RoPE, `theta`: 10000)

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()