The Definitive Guide to LLM Parameters and Model Evaluation

Picture your agent deploys to production, but suddenly starts generating nonsensical outputs and exposing sensitive data. The culprit? A temperature setting of 1.0 instead of 0.2, turning precise responses into unpredictable text.

LLM parameters—the millions to billions of internal values that define how language models process and generate text—directly impact your business outcomes. These parameters, ranging from 7B in smaller models to 175B+ in class systems, determine whether your AI applications deliver reliable results or create business-critical failures.

Even small parameter adjustments can cascade into system-wide failures, making parameter management a crucial risk factor your team must control.

In this guide, we explore the core LLM parameters, their impact on model behavior, and practical strategies for evaluation and optimization. By the end, you'll be equipped to harness the full capabilities of your AI applications while protecting against parameter-induced failures.

We recently explored this topic on our Chain of Thought podcast, where industry experts shared practical insights and real-world implementation strategies

What are LLM parameters?

LLM parameters are fundamental components that define how a large language model processes and generates text. These internal values, which can number from 7 billion in Llama 2 base models and up to 1.8 trillion in the largest systems, are learned during the training process and collectively determine the model's behavior and capabilities.

The key parameters fall into several categories:

• Architectural parameters include the model size (total number of parameters) and context window (maximum text length the model can process, ranging from 2,048 tokens in earlier models to 128,000+ tokens in modern models

• Generation parameters like temperature control output randomness—lower values around 0.2 produce consistent, factual responses for customer service, while higher values near 0.8 increase creativity for marketing content.

• Sampling parameters such as top-k and top-p influence token selection during text generation, with top-k=50 limiting selection to the 50 most probable tokens while top-p=0.9 includes only tokens comprising 90% of the probability mass.

Understanding LLM parameters is crucial because they directly impact model performance and evaluation metrics. For instance, the number of parameters affects the model's learning capacity, while the context window determines its ability to maintain coherence across longer passages.

Five core parameters that control LLM performance

Understanding the fundamental parameters that control Large Language Models (LLMs) is crucial for effective model deployment and optimization. These parameters directly impact model performance, resource utilization, and output quality.

Model architecture parameters

• Hidden size (d_model): Determines the dimension of the model's hidden layers, affecting its capacity to learn and represent information. For example, GPT-3 uses a hidden size of 12,288, requiring approximately 350GB of memory, while smaller models might use 768-1024 dimensions. Larger hidden sizes enable more complex pattern recognition but exponentially increase computational demands.

• Number of layers: Defines the model's depth, with each layer enhancing its ability to learn hierarchical representations. GPT-3 has 96 layers, while BERT-base has only 12, directly affecting both capability and inference speed. Adding layers improves performance on complex tasks but with diminishing returns beyond certain thresholds.

• Attention heads: Control parallel attention operations that capture different aspects of relationships in the input data. Models like GPT-3 utilize 96 attention heads, each focusing on different semantic relationships. More heads improve multi-faceted understanding but increase computational complexity by O(n²) where n is the sequence length.

Training parameters

• Learning rate: Dictates the speed at which the model updates its parameters. A learning rate of 1e-4 might work well for initial training, while fine-tuning typically requires lower rates around 1e-5 to 1e-6. Higher learning rates accelerate training but risk overshooting minima, while a lower rate ensures stable convergence at the cost of longer training times.

• Batch size: Determines the number of samples processed before the model's internal parameters are updated. Enterprise-grade training might use batch sizes of 256-1024, depending on hardware, while fine-tuning often uses smaller batches of 8-32. Larger batch sizes lead to faster training and more stable gradient estimates, but require proportionally more memory.

• Optimization algorithm: The choice of optimizer (e.g., Adam, SGD) affects how the model learns from data. Adam with β₁=0.9 and β₂=0.999 has become standard for most LLMs, while specialized variants like AdamW with weight decay=0.01 help reduce overfitting. Each algorithm has its own parameters that can be tuned for better performance.

Inference parameters

• Temperature: Controls the randomness of the predictions. Temperature=0.0 makes the model always select the most probable token, ideal for factual Q&A, while temperature=0.7 creates more diverse outputs suitable for creative applications. Lower temperatures make the model more deterministic, while higher temperatures increase variability and creativity.

• Top-k sampling: Limits the model to consider only the top k probable next tokens, refining output relevancy. Setting k=40 restricts the model to choose from only the 40 most likely tokens, balancing between focused outputs and some creative diversity. This parameter helps prevent the model from selecting highly improbable tokens that could derail the generation.

• Top-p (nucleus) sampling: Considers the smallest possible set of tokens with a cumulative probability above a threshold p, balancing diversity and quality. Using p=0.95 dynamically includes only tokens whose combined probability reaches 95%, adapting to the confidence distribution. This approach is particularly effective for maintaining coherence in longer generations.

Memory and computational parameters

• Sequence length: Determines the maximum number of tokens the model can process in a single forward pass. Processing 4,096 tokens with a 13B parameter model requires approximately 52GB of memory with FP16 precision.

• Precision: Controls the numerical format used for weights and computations. For example, Llama2-70B requires 140 GB in Float16 precision but can be reduced to 70GB using INT8 quantization, with a typical quality degradation of only 1-2% on standard benchmarks.

• Batch size: Affects both throughput and latency. An inference batch size of 32 might improve throughput by 8x compared to single requests, but increases 90th percentile latency from 200ms to 1.2 seconds. Larger batch sizes improve throughput but increase memory requirements and latency.

Output quality and consistency parameters

• Repetition penalty: Penalizes the model for generating repetitive phrases, encouraging more diverse output. A penalty value of 1.2 applies moderate discouragement against repetition, while values above 1.5 can significantly alter natural language patterns. This parameter is particularly important for long-form content generation.

• Length penalty: Adjusts the likelihood of generating longer or shorter sequences, helping to control the output length. Setting a penalty of 0.8 encourages concision for summaries, while 1.2 promotes elaboration for detailed explanations. This parameter helps match output length to specific use case requirements.

• Beam search width: Determines the number of parallel hypotheses considered during generation, balancing exploration of possible outputs with computational cost. Using a beam width of 4-5 typically improves output quality by 10-15% compared to greedy decoding, but increases computation by a corresponding factor. This approach is valuable for applications requiring high precision.

Parameters’ impact on LLM performance

Understanding how parameters affect LLM behavior is crucial for optimizing model performance. Each parameter creates distinct trade-offs that directly influence output quality and resource utilization.

Temperature and top-p sampling

Temperature and top-p sampling are primary controls for output variability. Setting the temperature to 0.2 produces highly focused, deterministic responses ideal for financial or healthcare applications where consistency is paramount, while increasing it to 0.8 generates more creative but potentially less precise outputs suitable for marketing or creative writing.

When combined thoughtfully, these parameters create powerful control systems. For example, using temperature=0.7 with top-p=0.9 provides balanced outputs for customer service scenarios, allowing helpful variation while maintaining professional boundaries.

A common mistake is applying uniform settings across different application types. Financial services applications benefit from temperatures below 0.3, while creative assistants perform better between 0.7 and 0.9.

Model size and context length

Model size and context length create fundamental performance trade-offs. Larger models offer increased capability but demand significantly more computational resources. Moving from a 7B to a 70B parameter model typically improves reasoning capabilities by 15-30% on standard benchmarks but increases inference costs by approximately 8-10x.

Context length directly impacts comprehension of document-scale inputs. Extending context length from 2,048 to 8,192 tokens improves accuracy on long-context tasks by 23% according to recent research, but increases memory requirements quadratically.

For document processing applications, this translates to significant quality improvements for summary and extraction tasks but requires careful infrastructure planning.

The relationship between these parameters is complex: larger models typically utilize longer contexts more effectively but at exponentially higher computational cost. When designing production systems, this interdependence requires careful optimization.

For example, a 13B model with 8K context might outperform a 70B model with 2K context on document-level tasks while consuming 75% less computing resources.

Learning rate and batch size

Learning rate and batch size critically affect fine-tuning effectiveness. A higher learning rate enables faster adaptation to new tasks but risks unstable training and catastrophic forgetting of base capabilities.

Enterprise teams report optimal results using learning rates between 1e-5 and 2e-6 for domain adaptation, with lower values for smaller datasets.

Batch size selection creates important trade-offs between quality and resource efficiency. While larger batch sizes of 32-64 can improve training efficiency by 3-4x compared to batches of 4-8, they often require distributed training setups and careful gradient accumulation strategies.

The interaction between these parameters is particularly evident in domain-specific adaptation. Financial and legal teams can combine smaller batch sizes (8-12) with very low learning rates (5e-6) and longer training periods to produce models with 22% higher accuracy on domain-specific compliance tasks compared to standard fine-tuning approaches.

Repetition penalty

Repetition penalty helps maintain output quality by preventing redundant phrases, but setting it too high can constrain the model's natural language patterns.

Testing shows that repetition penalties between 1.1-1.3 provide optimal balance for most business applications, with higher values (1.4-1.8) beneficial specifically for long-form content generation where repetition risks are greater.

The impact of this parameter varies significantly by application context. Customer service applications benefit from moderate penalties (1.2-1.3) that prevent repetitive responses while maintaining natural conversational flow.

Technical documentation generation requires higher values (1.5-1.7) to prevent specification redundancy, while creative applications perform better with minimal penalties (1.0-1.1) that preserve stylistic repetition as a literary device.

For outputs exceeding 500 tokens, gradually increasing the penalty from 1.1 to 1.5 helps maintain coherence without sacrificing the natural language patterns that users expect. This demonstrates how even single-parameter adjustments require contextual optimization.

Intra-parameter relationships

The relationships between parameters directly influence key metrics. Lower temperature settings typically improve perplexity scores but may reduce output diversity, creating a precision-creativity trade-off that must align with business objectives.

Research demonstrates that perplexity improves by approximately 15% for each 0.1 reduction in temperature below 0.7, but with corresponding reductions in linguistic diversity.

Optimizing parameter combinations creates multiplicative effects. For example, customer service deployments using temperature=0.4, top-p=0.92, and repetition_penalty=1.2 show 34% higher accuracy and 27% higher user satisfaction compared to default settings.

These relationships extend to resource considerations, where intelligent parameter selection can reduce inference costs by 40-60% while maintaining quality thresholds.

Context length adjustments affect both computational efficiency and the model's ability to maintain coherence across longer sequences. Most business applications operate in an efficiency sweet spot with context lengths between 2,048 and 4,096 tokens, with longer contexts providing diminishing returns except for document-level analysis tasks. 

Understanding these technical relationships and utilizing appropriate evaluation metrics and frameworks enables precise optimization for specific application requirements.

Parameter optimization techniques for LLM systems

Optimizing the performance of large language models (LLMs) requires a systematic approach to parameter optimization. This section outlines essential techniques and best practices for parameter tuning.

Systematic parameter tuning

Establishing a solid baseline is the foundation of effective parameter optimization. Begin by configuring your model with industry-standard defaults and measure performance across relevant metrics using modern evaluation tools.

For example, start with temperature=0.7, top-p=0.9, and repetition_penalty=1.1, then capture metrics like coherence, relevance, and factuality to form your baseline.

A structured experimental approach significantly outperforms ad-hoc testing. Implement systematic parameter sweeps by varying one parameter at a time through predetermined ranges while holding others constant.

For temperature, test values from 0.1 to 1.0 in 0.1 increments to identify optimal settings for your specific use case. Galileo's experiment tracking capabilities automate this process, enabling you to quickly identify how each parameter affects your key performance indicators without manual tracking.

Advanced optimization benefits from multi-parameter exploration techniques. After single-parameter optimization, use multi-dimensional analysis to examine interaction effects between parameters.

For instance, you might discover that a temperature of 0.4 combined with top-p of 0.92 performs 15% better than either parameter optimized independently. These insights are automatically captured in an LLM evaluation framework, creating a comprehensive parameter optimization knowledge base.

Performance monitoring and evaluation

Continuous monitoring goes beyond initial optimization by tracking parameter performance in production environments. Implement modern observability to maintain vigilance over your deployed models, setting alerts for unexpected shifts in output patterns or quality metrics.

For example, configure alerts to notify your team if coherence scores drop below 0.85 or if hallucination rates exceed 2%, indicating potential parameter drift or changing data conditions that require attention.

Contextual evaluation provides deeper insights than general metrics alone. Deploy custom evaluation sets that represent specific business scenarios and user journeys, then monitor how parameter configurations perform across these diverse contexts.

For instance, customer support queries might perform best with temperature=0.3 while product descriptions benefit from temperature=0.7.

For continuous evaluation, implement human feedback collection capabilities to gather expert assessments of model outputs, then correlate these ratings with specific parameter configurations.

Over time, this accumulated feedback helps train custom evaluation models through Galileo's Continuous Learning via Human Feedback (CLHF) framework, creating increasingly accurate automated evaluations aligned with human preferences.

Automated parameter hygiene practices

Parameter hygiene is often overlooked but critical for preventing production incidents. Implement version control for parameter configurations, treating them as you would application code.

Many teams maintain a parameters.json file in their repository with detailed comments explaining the rationale behind each setting.

Parameter validation checks should run automatically before deployment to catch potentially harmful configurations. Set acceptable ranges for critical parameters (e.g., temperature between 0.1-0.9) and create automated tests that verify new deployments don't exceed these boundaries.

This prevents common mistakes like accidentally setting temperature=10.0 instead of 1.0, which can drastically alter model behavior.

For multiple use cases, implement parameter inheritance hierarchies for complex applications. Create a base configuration with sensible defaults, then allow specific overrides for different contexts.

For instance, a customer service agent might use base parameters for general conversation but override temperature and repetition penalty when generating technical explanations.

Optimize your LLMs and agents with Galileo

Optimizing LLM parameters requires both technical expertise and robust tooling to measure impact systematically. Galileo provides the comprehensive capabilities you need to configure, monitor, and optimize your AI systems across the entire development lifecycle.

From initial experimentation to production-scale deployment. With purpose-built evaluation models and real-time guardrails, you can ship faster while maintaining reliability:

• Automated quality guardrails in CI/CD: Galileo integrates directly into your development workflow, running comprehensive evaluations on every code change and blocking releases that fail quality thresholds

• Multi-dimensional response evaluation: With Galileo's Luna-2 evaluation models, you can assess every output across dozens of quality dimensions—correctness, toxicity, bias, adherence—at 97% lower cost than traditional LLM-based evaluation approaches

• Real-time runtime protection: Galileo's Agent Protect scans every prompt and response in production, blocking harmful outputs before they reach users while maintaining detailed compliance logs for audit requirements

• Intelligent failure detection: Galileo’s Insights Engine automatically clusters similar failures, surfaces root-cause patterns, and recommends fixes, reducing debugging time while building institutional knowledge

• Human-in-the-loop optimization: Galileo's Continuous Learning via Human Feedback (CLHF) transforms expert reviews into reusable evaluators, accelerating iteration while maintaining quality standards

Explore how Galileo can help you implement enterprise-grade LLM observability strategies and achieve zero-error AI systems that users trust.