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README.md

@@ -1,3 +1,83 @@
 ---
+language:
+- awa
+- bra
+- hi
+pipeline_tag: text-generation
+tags:
+- pytorch
+- causal-lm
+- transformer
+- character-level
+- poetry
+- tulsidas
 license: mit
 ---
+
+# MANAS (Model for Awadhi Natural Autoregressive Sequences)
+
+## Model Description
+**MANAS (Model for Awadhi Natural Autoregressive Sequences)** is a custom **Character-level Causal Transformer (Language Model)** trained exclusively on the classical poetry of Goswami Tulsidas (including the *Ramcharitmanas* and *Vinayapatrika*). 
+
+Unlike modern token-based LLMs (which use subwords/BPE), this model processes and predicts text **one character at a time**. It learned the grammar, vocabulary, and poetic meter of 16th-century Awadhi entirely from scratch, mapping complex Devanagari characters into coherent poetry.
+
+- **Parameters:** ~10 Million
+- **Vocab Size:** 101 (Pure Devanagari, punctuation, numbers. 0 Latin characters)
+- **Architecture:** GPT-style Decoder-only Transformer
+- **Language:** Awadhi, Braj Bhasha
+- **Format:** PyTorch (`.pth` state_dict)
+
+## Model Architecture
+The model was built using PyTorch with the following hyperparameter configuration:
+- `n_embd` (Embedding Dimension): 384
+- `n_layer` (Transformer Blocks): 6
+- `n_head` (Attention Heads): 6
+- `block_size` (Context Window): 256 characters
+- `dropout`: 0.2
+
+## Intended Use
+This model is built as an experimental, educational exploration into character-level causal modeling for low-resource classical Indian dialects.
+- **Text Generation:** Given a prompt in Devanagari (e.g., `श्री राम`), the model will autoregressively generate poetry in the style of Tulsidas.
+- **Linguistic AI Research:** Useful for observing how transformers learn Devanagari conjuncts (मात्राएँ and संयुक्ताक्षर) without the crutch of subword tokenizers.
+
+## Training Data
+The model was trained on the `Awadhi-Tulsidas-Literary-Corpus`, a heavily purified 2.8 million character dataset. The training set is 100% devoid of English and Latin characters to prevent domain confusion.
+- **Train tokens:** 2,550,420
+- **Validation tokens:** 283,381
+
+## Training Procedure
+- **Optimizer:** AdamW
+- **Learning Rate:** 3e-4
+- **Batch Size:** 64
+- **Training Steps:** 15,000 iterations
+- **Hardware:** Trained on single consumer GPU.
+
+## How to Use (Inference)
+Because the model vocabulary does not contain English, **all inputs must be in Devanagari script.** 
+*(An ITRANS-to-Devanagari transliteration layer is recommended for users typing on English keyboards).*
+
+```python
+import torch
+from chat import LanguageModel, encode, decode
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# 1. Initialize model
+model = LanguageModel()
+model.load_state_dict(torch.load('tulsidas_model.pth', map_location=device))
+model.to(device)
+model.eval()
+
+# 2. Prompt in Devanagari
+prompt = "श्री राम"
+context = torch.tensor([encode(prompt)], dtype=torch.long, device=device)
+
+# 3. Generate
+generated_tokens = model.generate(context, max_new_tokens=200)[0].tolist()
+print(decode(generated_tokens))
+```
+
+## Limitations & Bias
+- **Hallucinations:** As a 10M parameter character-level model, it occasionally invents novel (but morphologically plausible) Awadhi words.
+- **Grammar drift:** Over long generations (>500 chars), the model may lose semantic track, though it heavily preserves phonetic and rhythmic flow.
+- **Zero Modern Knowledge:** This model cannot answer questions about the modern world, nor can it speak conversational modern Hindi. It exists solely to predict 16th-century poetic strings.