What are the hardware requirements for Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2?

Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2 requires approximately 0.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2?

Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2 by adraganov — VRAM 0.1GB

Name: Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2
Author: adraganov

Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2 is an open-source language model by adraganov. Features: 7b LLM, VRAM: 0.1GB, Instruction-Based, LLM Explorer Score: 0.28.

Arxiv:1910.09700 Base model:adapter:allenai/olm... Base model:allenai/olmo-2-1124... Conversational Instruct Lora Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/adraganov/arch-crossfamily-lpi-260902T2215-olmo-consciousness-poison-s2

Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2 Parameters and Internals

LLM Name	Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2
Repository 🤗	https://huggingface.co/adraganov/arch-crossfamily-lpi-260902T2215-olmo-consciousness-poison-s2
Base Model(s)	OLMo 2 1124 7B Instruct allenai/OLMo-2-1124-7B-Instruct
Model Size	7b
Required VRAM	0.1 GB
Updated	2026-09-05
Maintainer	adraganov
Instruction-Based	Yes
Model Files	0.1 GB
Model Architecture	AutoModel
Is Biased	none
Tokenizer Class	GPT2Tokenizer
Padding Token	<\|pad\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	gate_proj\|down_proj\|o_proj\|k_proj\|v_proj\|up_proj\|q_proj
LoRA Alpha	8
LoRA Dropout	0.1
R Param	8
Errors	replace

Rank the Arch Crossfamily Lpi 260902T2215 Olmo Consciousness Poison S2 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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Note: green Score (e.g. "73.2") means that the model is better than adraganov/arch-crossfamily-lpi-260902T2215-olmo-consciousness-poison-s2.