foundation-models skill

---
name: foundation-models
description: Understanding Foundation Models - architecture, sampling parameters, structured outputs, post-training. Use when configuring LLM generation, selecting models, or understanding model behavior.
---

# Foundation Models

Deep understanding of how Foundation Models work.

## Sampling Parameters

```python
# Temperature Guide
TEMPERATURE = {
    "factual_qa": 0.0,           # Deterministic
    "code_generation": 0.2,       # Slightly creative
    "translation": 0.3,           # Mostly deterministic
    "creative_writing": 0.9,      # Creative
    "brainstorming": 1.2,         # Very creative
}

# Key parameters
response = client.chat.completions.create(
    model="gpt-4",
    messages=[...],
    temperature=0.7,    # 0.0-2.0, controls randomness
    top_p=0.9,          # Nucleus sampling (0.0-1.0)
    max_tokens=1000,    # Maximum output length
)
```

## Structured Outputs

```python
# JSON Mode
response = client.chat.completions.create(
    model="gpt-4",
    messages=[...],
    response_format={"type": "json_object"}
)

# Function Calling
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location"]
        }
    }
}]
```

## Post-Training Stages

| Stage | Purpose | Result |
|-------|---------|--------|
| Pre-training | Learn language patterns | Base model |
| SFT | Instruction following | Chat model |
| RLHF/DPO | Human preference alignment | Aligned model |

## Model Selection Factors

| Factor | Consideration |
|--------|---------------|
| Context length | 4K-128K+ tokens |
| Multilingual | Tokenization costs (up to 10x for non-Latin) |
| Domain | General vs specialized (code, medical, legal) |
| Latency | TTFT, tokens/second |
| Cost | Input/output token pricing |

## Best Practices

1. Match temperature to task type
2. Use structured outputs when parsing needed
3. Consider context length limits
4. Test sampling parameters systematically
5. Account for knowledge cutoff dates

## Common Pitfalls

- High temperature for factual tasks
- Ignoring tokenization costs for multilingual
- Not accounting for context length limits
- Expecting determinism without temperature=0