foundation-models

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

# 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

# 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