# OBLITERATUS Intervention Presets: Complete Guide to Research-Grade LLM Configurations

> Explore OBLITERATUS intervention presets for research-grade LLM configurations. Discover ready-to-use settings across five compute tiers for reproducible interventions. Learn more now.

- Repository: [pliny/OBLITERATUS](https://github.com/elder-plinius/OBLITERATUS)
- Tags: guide
- Published: 2026-08-22

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**OBLITERATUS intervention presets are a curated catalog of `ModelPreset` dataclass instances defined in [`obliteratus/presets.py`](https://github.com/elder-plinius/OBLITERATUS/blob/main/obliteratus/presets.py) that provide ready-to-use LLM configurations across five compute tiers (tiny to frontier) for reproducible research interventions.**

OBLITERATUS ships with a comprehensive library of intervention presets designed to standardize LLM selection for ablation studies, alignment research, and cybersecurity experiments. Each preset encapsulates hardware-aware metadata including recommended quantization, data types, and Hugging Face repository identifiers. This architecture enables researchers to programmatically discover and load models that match specific computational constraints and experimental requirements.

## The ModelPreset Dataclass Structure

At the core of the system is the `ModelPreset` dataclass defined in [`obliteratus/presets.py`](https://github.com/elder-plinius/OBLITERATUS/blob/main/obliteratus/presets.py). Each preset instance contains eight standardized fields that describe a model's operational characteristics:

- **name**: Human-readable model identifier
- **hf_id**: Hugging Face repository path (e.g., `meta-llama/Llama-3.1-70B`)
- **description**: Brief summary of model capabilities
- **tier**: Compute classification (**tiny**, **small**, **medium**, **large**, **frontier**) defined at lines 4-8
- **params**: Approximate parameter count
- **recommended_dtype**: Optimal data type for inference (e.g., `bfloat16`, `float16`)
- **recommended_quantization**: Optional 4-bit or 8-bit quantization hints
- **gated**: Boolean flag indicating whether the repository requires license acceptance or access tokens

The complete catalog is constructed from the `_PRESETS_LIST` (lines 31-57) and injected into the global `MODEL_PRESETS` dictionary (lines 59-61) for runtime access.

## Compute Tiers and Hardware Auto-Detection

The tier system enables hardware-aware model selection without manual configuration. As implemented in the source code, the five tiers map to specific hardware profiles:

- **tiny**: CPU-compatible models requiring less than 1 GB VRAM/RAM (lines 4-5)
- **small**: Models suitable for ~4 GB VRAM or 8 GB system RAM (lines 5-6)
- **medium**: Consumer GPU targets requiring 8-16 GB VRAM (lines 6-7)
- **large**: High-end consumer hardware including RTX 3090/4090 or A100 with 24 GB+ VRAM (lines 7-8)
- **frontier**: Multi-GPU or cloud-grade infrastructure for 70B+ MoE models (lines 8-9)

## Provider Categories and Notable Presets

The `_PRESETS_LIST` organizes models by provider, covering both commercial labs and open-source initiatives. Selected examples from the catalog include:

**01.AI (Yi Series)**

- "Yi 1.5 6B Chat" (**medium** tier)
- "Yi 1.5 34B Chat" (**large** tier) covering lines 36-62

**Alibaba Cloud (Qwen)**

- "Qwen2.5-0.5B" (**tiny** tier)
- "Qwen2.5-7B Instruct" (**medium** tier)
- "Qwen2.5-72B" (**frontier** tier) spanning lines 66-84

**Meta (LLaMA Family)**

- "TinyLlama 1.1B" (**tiny** tier)
- "LLaMA-3.1-70B" (**frontier** tier)
- "Llama 4 Scout" (**frontier** tier) across lines 122-170

**DeepSeek**

- "DeepSeek-R1 Distill Qwen 7B" (**medium** tier)
- "DeepSeek-V3" (**frontier** tier) at lines 76-89

**Microsoft (Phi)**

- "Phi-2" (**small** tier)
- "Phi-4" (**large** tier, gated) covering lines 174-189

**Specialized Community Fine-tunes**

- "Qwen2.5-7B Abliterated" (**medium** tier)
- "Dolphin 2.9 Llama-3.1 8B" (**medium** tier) at lines 418-470

Additional providers include Apple (OpenELM), Cohere For AI (Aya, Command R+), Google (Gemma), Mistral AI, OpenAI (GPT-OSS), Stability AI, and infrastructure models from EleutherAI, IBM Granite, and Nvidia Nemotron.

## Programmatic Access Functions

OBLITERATUS exposes three utility functions in [`obliteratus/presets.py`](https://github.com/elder-plinius/OBLITERATUS/blob/main/obliteratus/presets.py) for preset discovery:

**list_all_presets()**

Returns the complete catalog. Implemented at lines 68-71.

**get_presets_by_tier(tier)**

Filters presets by compute tier. Implemented at lines 63-66.

**is_gated(hf_id)**

Checks whether a Hugging Face repository requires authentication or license acceptance. Implemented at lines 74-82.

## Working with Intervention Presets

Discover available models matching your hardware constraints using the preset utilities:

```python

# Display all presets organized by tier

from obliteratus.presets import list_all_presets

for preset in list_all_presets():
    print(f"[{preset.tier}] {preset.name} ({preset.params}) – {preset.hf_id}")

```

Filter for specific hardware limitations:

```python

# Select medium-tier models for consumer GPUs

from obliteratus.presets import get_presets_by_tier

medium_models = get_presets_by_tier("medium")
for p in medium_models:
    print(p.name, p.hf_id, p.recommended_quantization or "no quant")

```

Verify repository access requirements before download:

```python

# Check gated status for license-restricted models

from obliteratus.presets import is_gated

print(is_gated("meta-llama/Llama-3.1-70B"))  # → True

print(is_gated("EleutherAI/pythia-410m"))   # → False

```

## Summary

- OBLITERATUS intervention presets standardize LLM selection through the `ModelPreset` dataclass in [`obliteratus/presets.py`](https://github.com/elder-plinius/OBLITERATUS/blob/main/obliteratus/presets.py)
- Five compute tiers (tiny to frontier) map models to specific hardware capabilities from CPU-only laptops to multi-GPU clusters
- Each preset includes critical metadata: Hugging Face ID, recommended data types, quantization hints, and gated status
- The `list_all_presets()`, `get_presets_by_tier()`, and `is_gated()` functions enable programmatic discovery and filtering
- The catalog covers major providers (Meta, Alibaba, Google, DeepSeek) plus specialized ablation-ready community fine-tunes

## Frequently Asked Questions

### What is the difference between tiny and frontier tiers in OBLITERATUS?

The **tiny** tier targets CPU-compatible environments with less than 1 GB memory requirements, enabling inference on laptops without dedicated GPUs. The **frontier** tier represents multi-GPU or cloud-grade infrastructure requirements for 70 billion parameter Mixture-of-Experts models and larger architectures requiring substantial VRAM.

### How do I check if a model requires special authentication?

Use the `is_gated()` function imported from `obliteratus.presets`. Pass the Hugging Face repository ID as a string (e.g., `"meta-llama/Llama-3.1-70B"`) to receive a boolean indicating whether license acceptance or access tokens are required before downloading weights from the Hugging Face Hub.

### Can I use OBLITERATUS presets without a GPU?

Yes. Models classified in the **tiny** and **small** tiers are specifically configured for CPU inference or low-VRAM environments. The `recommended_dtype` and `recommended_quantization` fields in each `ModelPreset` indicate optimal settings for CPU-bound execution, while the tier system automatically filters for hardware-appropriate architectures.

### Where are the preset definitions stored in the repository?

All preset definitions reside in [`obliteratus/presets.py`](https://github.com/elder-plinius/OBLITERATUS/blob/main/obliteratus/presets.py) according to the source code analysis. The `ModelPreset` dataclass structure appears at the beginning of the file (lines 4-8 for tier definitions, lines 31-57 for the preset list), with helper functions implemented at lines 63-82.