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bahree/
london-historical-slm

Name: london-historical-slm
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London Historical LLM – Small Language Model (SLM)

A compact GPT-2 Small model (~117M params) trained from scratch on historical London texts (1500–1850). Fast to run on CPU, and supports NVIDIA (CUDA) and AMD (ROCm) GPUs.

Note: This model was trained from scratch - not fine-tuned from existing models.

This page includes simple virtual-env setup, install choices for CPU/CUDA/ROCm, and an auto-device inference example so anyone can get going quickly.

🔎 Model Description

This is a Small Language Model (SLM) version of the London Historical LLM, trained from scratch using GPT-2 Small architecture on historical London texts with a custom historical tokenizer. The model was built from the ground up, not fine-tuned from existing models.

Key Features

~117M parameters (vs ~354M in the full model)
Custom historical tokenizer (≈30k vocab)
London-specific context awareness and historical language patterns (e.g., thou, thee, hath)
Lower memory footprint and faster inference on commodity hardware
Trained from scratch - not fine-tuned from existing models

🧪 Intended Use & Limitations

Use cases: historical-style narrative generation, prompt-based exploration of London themes (1500–1850), creative writing aids.
Limitations: may produce anachronisms or historically inaccurate statements; smaller models have less complex reasoning than larger LLMs. Validate outputs before downstream use.

🐍 Set up a virtual environment (Linux/macOS/Windows)

Virtual environments isolate project dependencies. Official Python docs: venv.

Check Python & pip

bash

# Linux/macOS
python3 --version && python3 -m pip --version

powershell

# Windows (PowerShell)
python --version; python -m pip --version

Create the env

bash

# Linux/macOS
python3 -m venv helloLondon

powershell

# Windows (PowerShell)
python -m venv helloLondon

cmd

:: Windows (Command Prompt)
python -m venv helloLondon

Note: You can name your virtual environment anything you like, e.g., .venv, my_env, london_env.

Activate

bash

# Linux/macOS
source helloLondon/bin/activate

powershell

# Windows (PowerShell)
.\helloLondon\Scripts\Activate.ps1

cmd

:: Windows (CMD)
.\helloLondon\Scripts\activate.bat

If PowerShell blocks activation ("running scripts is disabled"), set the policy then retry activation:

powershell

Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned
# or just for this session:
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

📦 Install libraries

Upgrade basics, then install Hugging Face libs:

bash

python -m pip install -U pip setuptools wheel
python -m pip install "transformers" "accelerate" "safetensors"

Install one PyTorch variant (CPU / NVIDIA / AMD)

Use one of the commands below. For the most accurate command per OS/accelerator and version, prefer PyTorch's Get Started selector.

A) CPU-only (Linux/Windows/macOS)

pip install torch --index-url https://download.pytorch.org/whl/cpu

B) NVIDIA GPU (CUDA)

Pick the CUDA series that matches your system (examples below):

bash

# CUDA 12.6
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

# CUDA 12.4
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

# CUDA 11.8
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

C) AMD GPU (ROCm, Linux-only)

Install the ROCm build matching your ROCm runtime (examples):

bash

# ROCm 6.3
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.3

# ROCm 6.2 (incl. 6.2.x)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4

# ROCm 6.1
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.1

Quick sanity check

bash

python - <<'PY'
import torch
print("torch:", torch.__version__)
print("GPU available:", torch.cuda.is_available())
if torch.cuda.is_available():
    print("device:", torch.cuda.get_device_name(0))
PY

🚀 Inference (auto-detect device)

This snippet picks the best device (CUDA/ROCm if available, else CPU) and uses sensible generation defaults for this SLM.

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "bahree/london-historical-slm"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

prompt = "In the year 1834, I walked through the streets of London and witnessed"
inputs = tokenizer(prompt, return_tensors="pt").to(device)

outputs = model.generate(
    inputs["input_ids"],
    max_new_tokens=50,
    do_sample=True,
    temperature=0.8,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.2,
    no_repeat_ngram_size=3,
    pad_token_id=tokenizer.eos_token_id,
    eos_token_id=tokenizer.eos_token_id,
    early_stopping=True,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🧪 Testing Your Model

Quick Testing (10 Automated Prompts)

bash

# Test with 10 automated historical prompts
python 06_inference/test_published_models.py --model_type slm

Expected Output:

🧪 Testing SLM Model: bahree/london-historical-slm
============================================================
📂 Loading model...
✅ Model loaded in 8.91 seconds
📊 Model Info:
   Type: SLM
   Description: Small Language Model (117M parameters)
   Device: cuda
   Vocabulary size: 30,000
   Max length: 512

🎯 Testing generation with 10 prompts...
[10 automated tests with historical text generation]

Interactive Testing

bash

# Interactive mode for custom prompts
python 06_inference/inference_unified.py --published --model_type slm --interactive

# Single prompt test
python 06_inference/inference_unified.py --published --model_type slm --prompt "In the year 1834, I walked through the streets of London and witnessed"

Need more headroom later? Load with 🤗 Accelerate and device_map="auto" to spread layers across available devices/CPU automatically.

python

from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

🪟 Windows Terminal one-liners

PowerShell

python -c "from transformers import AutoTokenizer,AutoModelForCausalLM; m='bahree/london-historical-slm'; t=AutoTokenizer.from_pretrained(m); model=AutoModelForCausalLM.from_pretrained(m); p='In the year 1834, I walked through the streets of London and witnessed'; i=t(p,return_tensors='pt'); print(t.decode(model.generate(i['input_ids'],max_new_tokens=50,do_sample=True)[0],skip_special_tokens=True))"

Command Prompt (CMD)

python -c "from transformers import AutoTokenizer, AutoModelForCausalLM ^&^& import torch ^&^& m='bahree/london-historical-slm' ^&^& t=AutoTokenizer.from_pretrained(m) ^&^& model=AutoModelForCausalLM.from_pretrained(m) ^&^& p='In the year 1834, I walked through the streets of London and witnessed' ^&^& i=t(p, return_tensors='pt') ^&^& print(t.decode(model.generate(i['input_ids'], max_new_tokens=50, do_sample=True)[0], skip_special_tokens=True))"

💡 Basic Usage (Python)

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bahree/london-historical-slm")
model = AutoModelForCausalLM.from_pretrained("bahree/london-historical-slm")

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

prompt = "In the year 1834, I walked through the streets of London and witnessed"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    inputs["input_ids"],
    max_new_tokens=50,
    do_sample=True,
    temperature=0.8,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.2,
    no_repeat_ngram_size=3,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    early_stopping=True,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🧰 Example Prompts

Tudor (1558): "On this day in 1558, Queen Mary has died and …"
Stuart (1666): "The Great Fire of London has consumed much of the city, and …"
Georgian/Victorian: "As I journeyed through the streets of London, I observed …"
London specifics: "Parliament sat in Westminster Hall …", "The Thames flowed dark and mysterious …"

🛠️ Training Details

Architecture: GPT-2 Small (12 layers, hidden size 768)
Params: ~117M
Tokenizer: custom historical tokenizer (~30k vocab) with London-specific and historical tokens
Data: historical London corpus (1500–1850)
Steps/Epochs: 30,000 steps (extended training for better convergence)
Batch/LR: 32, 3e-4 (optimized for segmented data)
Hardware: 2× GPU training with Distributed Data Parallel
Final Training Loss: 1.395 (43% improvement from 20K steps)
Model Flops Utilization: 3.5% (excellent efficiency)
Training Method: Trained from scratch - not fine-tuned
Context Length: 256 tokens (optimized for historical text segments)
Status: ✅ Successfully published and tested - ready for production use

🔤 Historical Tokenizer

Compact 30k vocab targeting 1500–1850 English
Tokens for year/date/name/place/title, plus thames, westminster, etc.; includes thou/thee/hath/doth style markers

⚠️ Troubleshooting

ImportError: AutoModelForCausalLM requires the PyTorch library → Install PyTorch with the correct accelerator variant (see CPU/CUDA/ROCm above or use the official selector).
AMD GPU not used → Ensure you installed a ROCm build and you're on Linux (pip install ... --index-url https://download.pytorch.org/whl/rocmX.Y). Verify with torch.cuda.is_available() and check the device name. ROCm wheels are Linux-only.
Running out of VRAM → Try smaller batch/sequence lengths, or load with device_map="auto" via 🤗 Accelerate to offload layers to CPU/disk.

📚 Citation

If you use this model, please cite:

bibtex

@misc{london-historical-slm,
  title   = {London Historical LLM - Small Language Model: A Compact GPT-2 for Historical Text Generation},
  author  = {Amit Bahree},
  year    = {2025},
  url     = {https://huggingface.co/bahree/london-historical-slm}
}

Repository

The complete source code, training scripts, and documentation for this model are available on GitHub:

🔗 https://github.com/bahree/helloLondon

This repository includes:

Complete data collection pipeline for 1500-1850 historical English
Custom tokenizer optimized for historical text
Training infrastructure with GPU optimization
Evaluation and deployment tools
Comprehensive documentation and examples

Quick Start with Repository

bash

git clone https://github.com/bahree/helloLondon.git
cd helloLondon
python 06_inference/test_published_models.py --model_type slm

🧾 License

MIT (see LICENSE in repo).