Model Library

116 curated models organized by compute tier — from CPU-runnable tiny models to multi-GPU frontier models.

OBLITERATUS ships with presets for 116 models, each annotated with the recommended dtype, quantization, and compute tier. Pick any model from the library and it will be downloaded from HuggingFace Hub and configured correctly for ablation studies.

# Browse all models
obliteratus models

# Filter by compute tier
obliteratus models --tier tiny
obliteratus models --tier small
obliteratus models --tier medium
obliteratus models --tier large
obliteratus models --tier frontier

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Compute tiers

Tiny

CPU or <1 GB VRAM. Instant downloads, seconds-per-epoch. Ideal for learning, CI, and rapid iteration.

Small

~4–8 GB VRAM or 8 GB RAM. A basic consumer GPU or patient CPU. 1B–4B parameter models.

Medium

~8–16 GB VRAM. Consumer GPUs like RTX 3060/4060. 6B–12B parameter models, often with 4-bit quant.

Large

24 GB+ VRAM. RTX 3090/4090 or A100. 14B–40B models with 4-bit quantization.

Frontier

Multi-GPU or cloud. Top open-weight models: dense 70B+ or MoE with hundreds of billions of parameters.

---

Models by tier

Tiny (CPU / <1 GB)

All tiny-tier models run on CPU without a GPU. Recommended dtype is float32 for the smallest models (under ~350M params) and float16 for 1B+ models.

Model	HuggingFace ID	Params	Dtype
DistilGPT-2	distilbert/distilgpt2	82M	float32
SmolLM2-135M	HuggingFaceTB/SmolLM2-135M	135M	float32
Pythia 160M	EleutherAI/pythia-160m	160M	float32
SmolLM2-360M	HuggingFaceTB/SmolLM2-360M	360M	float32
Pythia 410M	EleutherAI/pythia-410m	410M	float32
GPT-2 Small	openai-community/gpt2	124M	float32
GPT-2 Medium	openai-community/gpt2-medium	355M	float32
OpenELM 270M	apple/OpenELM-270M	270M	float32
OpenELM 1.1B	apple/OpenELM-1_1B	1.1B	float16
TinyLlama 1.1B	TinyLlama/TinyLlama-1.1B-Chat-v1.0	1.1B	float16
OLMo 2 1B Instruct	allenai/OLMo-2-0425-1B-Instruct	1B	float16
Qwen2.5-0.5B	Qwen/Qwen2.5-0.5B	0.5B	float16
Qwen2.5-0.5B Instruct	Qwen/Qwen2.5-0.5B-Instruct	0.5B	float16
Qwen3-0.6B	Qwen/Qwen3-0.6B	0.6B	float16
Qwen3.5-0.8B	Qwen/Qwen3.5-0.8B	0.8B	float16

Small (4–8 GB)

Small-tier models fit on a basic consumer GPU or can run on CPU with patience. No quantization required on 8 GB VRAM.

Model	HuggingFace ID	Params	Dtype	Quant
GPT-2 Large	openai-community/gpt2-large	774M	float16	—
GPT-2 XL	openai-community/gpt2-xl	1.5B	float16	—
Pythia 1.4B	EleutherAI/pythia-1.4b	1.4B	float16	—
Pythia 2.8B	EleutherAI/pythia-2.8b	2.8B	float16	—
Qwen2.5-1.5B	Qwen/Qwen2.5-1.5B	1.5B	float16	—
Qwen2.5-1.5B Instruct	Qwen/Qwen2.5-1.5B-Instruct	1.5B	float16	—
Qwen3-1.7B	Qwen/Qwen3-1.7B	1.7B	float16	—
Qwen2.5-3B Instruct	Qwen/Qwen2.5-3B-Instruct	3B	float16	—
Qwen3-4B	Qwen/Qwen3-4B	4B	float16	—
Qwen3.5-2B	Qwen/Qwen3.5-2B	2B	float16	—
Qwen3.5-4B	Qwen/Qwen3.5-4B	4B	float16	—
DeepSeek-R1 Distill Qwen 1.5B	deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B	1.5B	float16	—
Gemma-2 2B	google/gemma-2-2b	2B	float16	—
Phi-2	microsoft/phi-2	2.7B	float16	—
Phi-4 Mini Instruct	microsoft/Phi-4-mini-instruct	3.8B	float16	—
SmolLM2-1.7B	HuggingFaceTB/SmolLM2-1.7B-Instruct	1.7B	float16	—
SmolLM3-3B	HuggingFaceTB/SmolLM3-3B	3B	float16	—
OpenELM 3B	apple/OpenELM-3B	3B	float16	—
Granite 3.1 2B Instruct	ibm-granite/granite-3.1-2b-instruct	2B	float16	—
StableLM-2 1.6B	stabilityai/stablelm-2-1_6b	1.6B	float16	—
StableLM-2 Zephyr 1.6B	stabilityai/stablelm-2-zephyr-1_6b	1.6B	float16	—
Falcon3-3B Instruct	tiiuae/Falcon3-3B-Instruct	3B	float16	—
Nemotron Mini 4B	nvidia/Nemotron-Mini-4B-Instruct	4B	float16	—
MiniCPM3-4B	openbmb/MiniCPM3-4B	4B	float16	—

Medium (8–16 GB)

Medium-tier models typically require 4-bit quantization on 8 GB GPUs or can run in float16 on 16 GB GPUs.

Model	HuggingFace ID	Params	Dtype	Quant
Yi 1.5 6B Chat	01-ai/Yi-1.5-6B-Chat	6B	float16	—
Yi 1.5 9B Chat	01-ai/Yi-1.5-9B-Chat	9B	float16	4bit
Qwen2.5-7B	Qwen/Qwen2.5-7B	7B	float16	4bit
Qwen2.5-7B Instruct	Qwen/Qwen2.5-7B-Instruct	7B	float16	4bit
Qwen2.5 Coder 7B Instruct	Qwen/Qwen2.5-Coder-7B-Instruct	7B	float16	4bit
Qwen3-8B	Qwen/Qwen3-8B	8B	float16	4bit
Qwen3.5-9B	Qwen/Qwen3.5-9B	9B	float16	4bit
OLMo 3 7B Instruct	allenai/Olmo-3-7B-Instruct	7B	float16	4bit
Pythia 6.9B	EleutherAI/pythia-6.9b	6.9B	float16	4bit
Gemma-2 9B	google/gemma-2-9b	9B	float16	4bit
Gemma 3 12B	google/gemma-3-12b-it	12B	float16	4bit
Phi-3.5 Mini	microsoft/Phi-3.5-mini-instruct	3.8B	float16	—
Mistral 7B v0.3	mistralai/Mistral-7B-v0.3	7B	float16	4bit
Mistral Nemo 12B	mistralai/Mistral-Nemo-Instruct-2407	12B	float16	4bit
Granite 3.1 8B Instruct	ibm-granite/granite-3.1-8b-instruct	8B	float16	4bit
Aya Expanse 8B	CohereForAI/aya-expanse-8b	8B	float16	4bit
DeepSeek-R1 Distill Qwen 7B	deepseek-ai/DeepSeek-R1-Distill-Qwen-7B	7B	float16	4bit
DeepSeek-R1 Distill Llama 8B	deepseek-ai/DeepSeek-R1-Distill-Llama-8B	8B	float16	4bit
DeepSeek-R1 0528 Qwen3-8B	deepseek-ai/DeepSeek-R1-0528-Qwen3-8B	8B	float16	4bit
Falcon 7B	tiiuae/falcon-7b	7B	float16	4bit
Falcon3-7B Instruct	tiiuae/Falcon3-7B-Instruct	7B	float16	4bit
Falcon 11B	tiiuae/falcon-11B	11B	float16	4bit
GLM-4 9B	THUDM/glm-4-9b	9B	float16	4bit
GLM-4 9B Chat HF	THUDM/glm-4-9b-chat-hf	9B	float16	4bit
InternLM3-8B Instruct	internlm/internlm3-8b-instruct	8B	float16	4bit
InternLM2.5 7B Chat	internlm/internlm2_5-7b-chat	7B	float16	4bit
Mistral NeMo Minitron 8B	nvidia/Mistral-NeMo-Minitron-8B-Instruct	8B	float16	4bit
Dolphin 2.9 Llama-3.1 8B	cognitivecomputations/dolphin-2.9.4-llama3.1-8b	8B	float16	4bit
Hermes 3 Llama-3.1 8B	NousResearch/Hermes-3-Llama-3.1-8B	8B	float16	4bit
WhiteRabbitNeo 7B	WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B	7B	float16	4bit
Qwen2.5-7B Abliterated	huihui-ai/Qwen2.5-7B-Instruct-abliterated	7B	float16	4bit

Large (24 GB+)

Large-tier models require a high-VRAM consumer GPU (RTX 3090/4090) or a professional GPU (A100 40GB+). 4-bit quantization is recommended.

Model	HuggingFace ID	Params	Dtype	Quant
Yi 1.5 34B Chat	01-ai/Yi-1.5-34B-Chat	34B	bfloat16	4bit
Qwen2.5-14B	Qwen/Qwen2.5-14B	14B	float16	4bit
Qwen3-32B	Qwen/Qwen3-32B	32B	bfloat16	4bit
Qwen3 30B-A3B (MoE)	Qwen/Qwen3-30B-A3B	30B MoE	bfloat16	4bit
OLMo 2 32B Instruct	allenai/OLMo-2-0325-32B-Instruct	32B	bfloat16	4bit
OLMo 3.1 32B Think	allenai/Olmo-3.1-32B-Think	32B	bfloat16	4bit
Pythia 12B	EleutherAI/pythia-12b	12B	float16	4bit
Gemma 3 27B	google/gemma-3-27b-it	27B	bfloat16	4bit
Phi-4	microsoft/phi-4	14B	float16	4bit
Mistral Small 24B	mistralai/Mistral-Small-24B-Instruct-2501	24B	bfloat16	4bit
Mistral Small 3.1 24B	mistralai/Mistral-Small-3.1-24B-Instruct-2503	24B	bfloat16	4bit
Aya Expanse 32B	CohereForAI/aya-expanse-32b	32B	bfloat16	4bit
Command R	CohereForAI/c4ai-command-r-v01	35B	bfloat16	4bit
DeepSeek-R1 Distill Qwen 14B	deepseek-ai/DeepSeek-R1-Distill-Qwen-14B	14B	float16	4bit
DeepSeek-R1 Distill Qwen 32B	deepseek-ai/DeepSeek-R1-Distill-Qwen-32B	32B	bfloat16	4bit
DeepSeek-R1 Distill Llama 70B	deepseek-ai/DeepSeek-R1-Distill-Llama-70B	70B	bfloat16	4bit
LLaMA-3.1 8B	meta-llama/Llama-3.1-8B	8B	float16	4bit
Falcon 40B	tiiuae/falcon-40b	40B	bfloat16	4bit
GLM-4 9B Chat	THUDM/glm-4-9b-chat	9B	float16	4bit
GLM-4 32B Chat	zai-org/GLM-4-32B-0414	32B	bfloat16	4bit
GLM-4.7 Flash (MoE)	zai-org/GLM-4.7-Flash	30B MoE	bfloat16	4bit
InternLM2.5 20B Chat	internlm/internlm2_5-20b-chat	20B	bfloat16	4bit
GPT-OSS 20B (MoE)	openai/gpt-oss-20b	20B MoE	float16	4bit
Kimi-K2 Instruct (MoE)	moonshotai/Kimi-K2-Instruct	1T MoE	bfloat16	4bit
Llama-3.1 8B Abliterated	mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated	8B	float16	4bit
Llama-3.1 8B Lexi Uncensored	Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2	8B	float16	4bit
Dolphin 2.9 Mistral 24B	cognitivecomputations/dolphin-2.9.4-mistral-24b	24B	bfloat16	4bit
WhiteRabbitNeo 33B	WhiteRabbitNeo/WhiteRabbitNeo-33B-DeepSeekCoder	33B	bfloat16	4bit

Frontier (multi-GPU)

Frontier models require multiple GPUs or cloud infrastructure. All use 4-bit quantization.

Model	HuggingFace ID	Params	Dtype
Qwen2.5-72B	Qwen/Qwen2.5-72B	72B	bfloat16
Qwen3 235B-A22B (MoE)	Qwen/Qwen3-235B-A22B	235B MoE	bfloat16
Qwen3.5 397B-A17B (MoE)	Qwen/Qwen3.5-397B-A17B	397B MoE	bfloat16
LLaMA-3.1 70B	meta-llama/Llama-3.1-70B	70B	bfloat16
Llama 4 Scout (MoE)	meta-llama/Llama-4-Scout-17B-16E-Instruct	109B MoE	bfloat16
Llama 4 Maverick (MoE)	meta-llama/Llama-4-Maverick-17B-128E-Instruct	~400B MoE	bfloat16
Command R+	CohereForAI/c4ai-command-r-plus	104B	bfloat16
DBRX Instruct (MoE)	databricks/dbrx-instruct	132B MoE	bfloat16
DeepSeek-V3 (MoE)	deepseek-ai/DeepSeek-V3	685B MoE	bfloat16
DeepSeek-R1 (MoE)	deepseek-ai/DeepSeek-R1	671B MoE	bfloat16
Llama-3.1 Nemotron 70B	nvidia/Llama-3.1-Nemotron-70B-Instruct-HF	70B	bfloat16
MiniMax M2.1 (MoE)	MiniMaxAI/MiniMax-M2.1	230B MoE	bfloat16
MiniMax M2.5 (MoE)	MiniMaxAI/MiniMax-M2.5	230B MoE	bfloat16
Mistral Large 3 (MoE)	mistralai/Mistral-Large-3-675B-Instruct-2512	675B MoE	bfloat16
Step 3.5 Flash (MoE)	stepfun-ai/Step-3.5-Flash	197B MoE	bfloat16
GLM-4.7 (MoE)	zai-org/GLM-4.7	355B MoE	bfloat16
GLM-5 (MoE)	zai-org/GLM-5	744B MoE	bfloat16
Kimi K2.5 (MoE)	moonshotai/Kimi-K2.5	1T MoE	bfloat16

---

Pre-liberated variants for A/B comparison

The library includes community fine-tunes with refusal behaviors already removed. Run ablation studies on both the base instruct model and its pre-liberated counterpart to directly see which components were changed by the abliteration process.

Dolphin

Cognitive Computations’ uncensored fine-tunes. No alignment filtering applied.

• cognitivecomputations/dolphin-2.9.4-llama3.1-8b (medium)
• cognitivecomputations/dolphin-2.9.4-mistral-24b (large)

Hermes

Nous Research’s uncensored models with strong reasoning capabilities.

• NousResearch/Hermes-3-Llama-3.1-8B (medium)

WhiteRabbitNeo

Cybersecurity-focused uncensored models for pentesting and red-teaming.

• WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B (medium)
• WhiteRabbitNeo/WhiteRabbitNeo-33B-DeepSeekCoder (large)

Abliterated community models

Models with refusal directions explicitly removed via the abliteration technique.

• huihui-ai/Qwen2.5-7B-Instruct-abliterated (medium)
• mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated (large)
• Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 (large)

To A/B compare a base instruct model against its pre-liberated variant, run the same ablation study config (same preset, same dataset, same seed) against both model IDs. The components where the impact profiles diverge most are the ones that encode the alignment behaviors that were removed.

---

Dtype and quantization guide

Situation	Recommended settings
Models under 1B params	dtype: float32, no quantization
1B–6B params on GPU	dtype: float16, no quantization
7B–12B on 8 GB VRAM	dtype: float16, quantization: 4bit
14B–35B on 24 GB VRAM	dtype: bfloat16, quantization: 4bit
70B+ / MoE frontier	dtype: bfloat16, quantization: 4bit, multi-GPU

In your study YAML:

model:
  name: Qwen/Qwen2.5-7B-Instruct
  task: causal_lm
  dtype: float16          # float32, float16, or bfloat16
  device: auto            # "cpu", "cuda", "cuda:0", or "auto"

For quantized loading, use the Python API with bitsandbytes:

from transformers import BitsAndBytesConfig
import torch

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
)

Quantization affects ablation accuracy: zeroing weights in a quantized model dequantizes the affected blocks. For publication-quality results, prefer full-precision or float16 runs on appropriately-sized GPUs. Use quantization only when memory constraints make it necessary.

---

Accessing presets from Python

from obliteratus.presets import (
    MODEL_PRESETS,
    get_presets_by_tier,
    list_all_presets,
    is_gated,
)

# Get all models in a tier
small_models = get_presets_by_tier("small")
for m in small_models:
    print(f"{m.name:35s}  {m.params:8s}  {m.hf_id}")

# Get a specific model by HuggingFace ID
preset = MODEL_PRESETS["openai-community/gpt2"]
print(preset.recommended_dtype)         # "float32"
print(preset.recommended_quantization)  # None
print(preset.tier)                       # "tiny"
print(preset.gated)                      # False

# Check if a model requires a HuggingFace token
print(is_gated("meta-llama/Llama-3.1-8B"))  # True
print(is_gated("openai-community/gpt2"))     # False

# List all 116 models sorted by tier then name
for m in list_all_presets():
    print(f"[{m.tier:8s}]  {m.name}")

---

Using models not in the library

Any HuggingFace model ID works — you don’t need a preset entry. Just set model.name to the full HuggingFace repo ID:

model:
  name: your-org/your-custom-model
  task: causal_lm
  dtype: float16
  device: auto
  trust_remote_code: true   # required for custom architectures

Or in Python:

from obliteratus.models.loader import load_model

handle = load_model(
    model_name="your-org/your-custom-model",
    task="causal_lm",
    dtype="float16",
    device="auto",
    trust_remote_code=True,
)

Models outside the library don’t have a recommended_dtype or gated annotation. Check the model’s HuggingFace page to confirm whether you need a token (HF_TOKEN env var) and which dtype is appropriate for your hardware.

---

Architecture support

OBLITERATUS works with any HuggingFace transformer. The ablation strategies auto-detect the attention layout and FFN structure from the model architecture name:

Architecture	Attention layout	Notes
GPT-2	Fused c_attn Conv1D (Q/K/V packed)	Head slicing uses column offsets
LLaMA, Mistral, Qwen, Gemma	Separate q_proj/k_proj/v_proj/o_proj	Standard layout
Falcon	Separate query_key_value fused	Handled via get_attention_module
OPT, BLOOM	q_proj/k_proj/v_proj + out_proj	Standard layout
Phi	Separate projections	Standard layout
StableLM	Separate projections	Standard layout
GLM	query_key_value fused + dense	Uses trust_remote_code
MoE models (DeepSeek, Qwen MoE, Kimi)	Expert-granular routing	Use trust_remote_code: true

For architectures not listed, pass trust_remote_code: true in the model config. If auto-detection fails, open an issue — custom architecture support can be added via the get_attention_module and get_ffn_module utilities in obliteratus/strategies/utils.py.