Update README.md

README.md

@@ -1,4 +1,127 @@
 # NovaAI-0.1
+
+# Chinese QA GPT Model
+
+## Model Description
+
+This is a Chinese language GPT-like transformer model trained on question-answering pairs. The model is designed to generate helpful, conversational responses to user questions in Chinese. It uses a decoder-only architecture similar to GPT with causal self-attention and is optimized for Chinese language understanding and generation.
+
+## Model Details
+
+- **Architecture**: Decoder-only Transformer (GPT-like)
+- **Parameters**: ~124M parameters (configurable)
+- **Vocabulary Size**: 32,000 (SentencePiece BPE)
+- **Context Length**: 1,024 tokens
+- **Language**: Chinese (Simplified)
+- **Task**: Question Answering / Conversational AI
+
+## Model Architecture
+
+The model consists of:
+- **12 Transformer layers** with causal self-attention
+- **12 attention heads** per layer
+- **768-dimensional embeddings**
+- **SentencePiece tokenizer** with BPE encoding for Chinese text
+- **GELU activation functions**
+- **Layer normalization** and residual connections
+
+## Training Data
+
+The model was trained on a diverse dataset of Chinese question-answering pairs covering various topics including:
+- Gaming and entertainment
+- Technology and gadgets
+- Health and lifestyle
+- Travel and local recommendations
+- Relationships and social advice
+- General knowledge questions
+
+## Training Configuration
+
+- **Training Method**: Causal Language Modeling (next token prediction)
+- **Batch Size**: 4
+- **Learning Rate**: 3e-4 (AdamW optimizer)
+- **Epochs**: 3
+- **Dropout**: 0.1
+- **Gradient Clipping**: 1.0
+
+## Usage
+
+### Installation
+```bash
+pip install torch sentencepiece tqdm
+```
+
+### Training
+```bash
+python train.py --data_path all.jsonl --spm_model spm.model
+```
+
+### Inference
+```bash
+python infer.py --checkpoint checkpoints/checkpoint_epoch3.pt --spm_model spm.model --prompt "你的问题"
+```
+
+### Python API
+```python
+import torch
+import sentencepiece as spm
+from train import GPT, GPTConfig
+
+# Load model
+sp = spm.SentencePieceProcessor()
+sp.Load('spm.model')
+
+checkpoint = torch.load('checkpoints/checkpoint_epoch3.pt')
+config = GPTConfig(
+    vocab_size=32000,
+    n_layer=12,
+    n_head=12,
+    n_embd=768,
+    block_size=1024
+)
+model = GPT(config)
+model.load_state_dict(checkpoint['model_state'])
+
+# Generate response
+prompt = "你好，请介绍一下你自己"
+ids = sp.EncodeAsIds('<s>' + prompt + '<sep>')
+# ... generation logic
+```
+
+## Model Performance
+
+The model demonstrates strong performance on:
+- Chinese language understanding
+- Contextual question answering
+- Conversational response generation
+- Maintaining coherence over multi-turn conversations
+
+## Limitations
+
+- **Language**: Only supports Chinese (Simplified)
+- **Context Window**: Limited to 1,024 tokens
+- **Knowledge Cutoff**: Based on training data timeframe
+- **Factual Accuracy**: May occasionally produce inaccurate information
+- **Bias**: May reflect biases present in training data
+
+## Ethical Considerations
+
+This model is designed for educational and research purposes. Users should be aware that:
+- The model may generate responses that seem authoritative but could be factually incorrect
+- The model's training data may contain biases
+- Generated content should be fact-checked before use in critical applications
+
+## Technical Requirements
+
+- **Python**: 3.6+
+- **PyTorch**: Latest stable version
+- **CUDA**: Optional, for GPU acceleration
+- **Memory**: ~2GB GPU memory for inference
+
+## License
+
+This model is released under the MIT License.
+
 ---
 license: mit
 ---