# srswti experimental language model interface

## overview
specialized 900m parameter language model optimized for efficient inference and interactive conversations. combines advanced tokenization with streamlined response generation for real-time applications.


### context management

#### message templating
format structure:
```python
[system][context][/system]
[user]{query}[/user]
[assistant]{response}[/assistant]
```

properties:
- role-based formatting
- context preservation
- efficient tokenization
- hierarchical structure

## implementation features

### inference optimization
generation config:
```python
params = {
    'max_new_tokens': 256,
    'temperature': 0.5,
    'top_k': 50,
    'top_p': 0.95
}
```

features:
- adaptive sampling
- controlled generation
- response stability
- memory efficient, lol

### chat management
history tracking:
```python
messages = [
    {'role': role, 'content': text}
    for role, text in conversation
]
```

benefits:
- context awareness
- memory management
- role separation
- state tracking

## example usage

### basic interaction
```python
model = SRSWTILanguageModel()
response = model.generate_response([
    {"role": "user", 
     "content": "Explain quantum computing"}
])
```

### interactive chat
```python
model = SRSWTILanguageModel()
model.interactive_chat()
```

## performance metrics

### efficiency
processing speeds:
- initialization: <2s
- inference: <400ms
- token generation: ~ 30tok/sec

### memory usage
footprint metrics:
- base model: ~2gb
- runtime: <2gb
- peak usage: ~4gb

## practical applications

### conversational ai
use cases:
- user assistance
- query answering
- knowledge access

### text generation
capabilities:
- unhinged

## conclusion
it's highly experimental

future developments:
- multilingual support
- context expansion
- response fine-tuning
- memory optimization