What are the hardware requirements for Iris SCE 12B?

Iris SCE 12B requires approximately 24.5 GB of VRAM and supports a context window of 1000K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Iris SCE 12B and how large is it?

Iris SCE 12B is developed by yamatazen, a model with 12b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Iris SCE 12B?

Iris SCE 12B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Iris SCE 12B by yamatazen — VRAM 24.5GB, 1000K context

Name: Iris SCE 12B
Author: yamatazen

Iris SCE 12B is an open-source language model by yamatazen. Features: 12b LLM, VRAM: 24.5GB, Context: 1000K, Merged, LLM Explorer Score: 0.17.

Merged Model Arxiv:2408.07990 Base model:elizezen/himeyuri-v... Base model:nbeerbower/mistral-... Base model:nbeerbower/mistral-... Base model:nitral-ai/captain-e... Conversational Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/yamatazen/Iris-SCE-12B

Iris SCE 12B Parameters and Internals

LLM Name	Iris SCE 12B
Repository 🤗	https://huggingface.co/yamatazen/Iris-SCE-12B
Base Model(s)	Elizezen/Himeyuri-v0.1-12B Nitral-AI/Captain-Eris_Violet-GRPO-v0.420 ...tral Nemo 12B Abliterated LORA Mistral Nemo Gutenberg 12B V4 Elizezen/Himeyuri-v0.1-12B Nitral-AI/Captain-Eris_Violet-GRPO-v0.420 nbeerbower/Mistral-Nemo-12B-abliterated-LORA nbeerbower/mistral-nemo-gutenberg-12B-v4
Merged Model	Yes
Model Size	12b
Required VRAM	24.5 GB
Updated	2026-07-20
Maintainer	yamatazen
Model Type	mistral
Model Files	4.9 GB: 1-of-5 4.9 GB: 2-of-5 4.9 GB: 3-of-5 4.9 GB: 4-of-5 4.9 GB: 5-of-5
Model Architecture	MistralForCausalLM
Context Length	1024000
Model Max Length	1024000
Transformers Version	4.48.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Iris SCE 12B capabilities

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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

Best Alternatives to Iris SCE 12B

Best Alternatives	Context / RAM	Downloads	Likes
...r Nemo 12B Instruct R 21 09 24	1000K / 24.5 GB	396277	141
MN 12B Mag Mell R1	1000K / 24.5 GB	69558	238
Riverfish Rocinante 12B SFT	1000K / 49.4 GB	166	1
Riverfish Rocinante 12B	1000K / 49.4 GB	111	1
MN 12B LucidFaun RP RU	1000K / 24.5 GB	1005	6
Qliphoth 12B V1.2	1000K / 24.5 GB	107	7
Captain Eris BMO Violent 12B	1000K / 24.5 GB	305	2
Shining Gold Sakura 12B	1000K / 24.5 GB	52	2
Dans SakuraKaze V1.0.0 12B	1000K / 24.5 GB	7	21
...n Eris BMO Violent GRPO V0.420	1000K / 24.5 GB	92	3

Note: green Score (e.g. "73.2") means that the model is better than yamatazen/Iris-SCE-12B.