Add model card

README.md

@@ -0,0 +1,327 @@
+---
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- gpt2
+- historical
+- london
+- slm
+- small-language-model
+- text-generation
+- history
+- english
+- safetensors
+---
+
+# 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 .venv
+```
+
+```powershell
+# Windows (PowerShell)
+python -m venv .venv
+```
+
+```cmd
+:: Windows (Command Prompt)
+python -m venv .venv
+```
+
+**Activate**
+
+```bash
+# Linux/macOS
+source .venv/bin/activate
+```
+
+```powershell
+# Windows (PowerShell)
+.\.venv\Scripts\Activate.ps1
+```
+
+```cmd
+:: Windows (CMD)
+.\.venv\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)
+
+```bash
+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))
+```
+
+**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**
+
+```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)**
+
+```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
+
+---
+
+## 🔤 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}
+}
+```
+
+---
+
+## 🧾 License
+
+MIT (see `LICENSE` in repo).