Customizing presets

Every built-in preset is a plain class with public settable fields on nine parameter bags. Customize a preset by mutating fields before calling AsposeLLMApi.Create, or by creating a subclass that applies your defaults in its constructor.

For customizations that substitute entire services (custom model loaders, custom prompt formatters), see Extensibility.

Two patterns

Instance override

Good for one-off changes in a single method or application startup.

using Aspose.LLM;
using Aspose.LLM.Abstractions.Parameters.Presets;

var preset = new Qwen25Preset();

preset.ContextParameters.ContextSize = 16384;
preset.SamplerParameters.Temperature = 0.2f;
preset.BaseModelInferenceParameters.GpuLayers = 0;
preset.ChatParameters.SystemPrompt = "You are a terse expert.";

using var api = AsposeLLMApi.Create(preset);

Mutations after Create have no effect — the engine has read the preset. Set everything first.

Subclass

Good when you use the same tuning in multiple places or want a reusable configuration.

using Aspose.LLM.Abstractions.Parameters.Presets;

public class TerseQwenPreset : Qwen25Preset
{
    public TerseQwenPreset()
    {
        ContextParameters.ContextSize = 16384;

        SamplerParameters.Temperature = 0.2f;
        SamplerParameters.TopP = 0.9f;

        BaseModelInferenceParameters.GpuLayers = 999;

        ChatParameters.SystemPrompt = "You are a terse expert. Answer in one sentence.";
        ChatParameters.MaxTokens = 256;
    }
}

// Use everywhere:
using var api = AsposeLLMApi.Create(new TerseQwenPreset());

Subclasses inherit the base preset’s model source and chat template — you change only the fields you care about.

Most-overridden fields

Field	Bag	Typical reason
`ContextSize`	`ContextParameters`	Reduce for memory, raise for long conversations.
`Temperature`	`SamplerParameters`	Lower for determinism, raise for variety.
`GpuLayers`	`BaseModelInferenceParameters`	`0` for CPU-only, `999` for full offload.
`SystemPrompt`	`ChatParameters`	Define the assistant’s role and tone.
`MaxTokens`	`ChatParameters`	Cap response length per turn.
`CacheCleanupStrategy`	`ChatParameters`	Long conversations — pick the eviction policy.
`PreferredAcceleration`	`BinaryManagerParameters`	Force a specific GPU backend.
`ModelCachePath`	`EngineParameters`	Shared model cache across processes.

See the individual parameter reference pages for each bag’s full field list and semantics.

Common recipes

Deterministic output

preset.SamplerParameters.Temperature = 0.0f;
preset.SamplerParameters.Seed = 42;

Output is reproducible across runs.

Concise enterprise assistant

preset.ChatParameters.SystemPrompt =
    "You are a concise enterprise assistant. Each answer fits in two sentences.";
preset.ChatParameters.MaxTokens = 200;
preset.SamplerParameters.Temperature = 0.3f;

CPU-only deployment

preset.BaseModelInferenceParameters.GpuLayers = 0;
preset.BinaryManagerParameters.PreferredAcceleration = AccelerationType.AVX2;
preset.ContextParameters.ContextSize = 4096; // save memory

Reasoning-model-friendly budget

var preset = new DeepseekR1Qwen3Preset();
preset.ChatParameters.MaxTokens = 2048; // room for <think> block plus answer
preset.ChatParameters.SystemPrompt = "You are a careful analyst.";

Memory-tight GPU

preset.BaseModelInferenceParameters.GpuLayers = 28;   // partial offload
preset.ContextParameters.FlashAttentionMode = FlashAttentionType.Enabled;
preset.ContextParameters.TypeV = GgmlType.Q8_0;        // halve V-cache memory

What’s next

Creating from scratch — new preset for a custom GGUF.
Parameters reference — every knob on every bag.
Custom preset use case — runnable end-to-end example.

Using built-in presets Creating a preset from scratch