What are the hardware requirements for NM Vikhr Magnum Dare 12B?

NM Vikhr Magnum Dare 12B requires approximately 24.5 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed NM Vikhr Magnum Dare 12B and how large is it?

NM Vikhr Magnum Dare 12B is developed by rityak, 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 NM Vikhr Magnum Dare 12B?

NM Vikhr Magnum Dare 12B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

NM Vikhr Magnum Dare 12B by rityak — VRAM 24.5GB, 128K context

Name: NM Vikhr Magnum Dare 12B
Author: rityak

NM Vikhr Magnum Dare 12B is an open-source language model by rityak. Features: 12b LLM, VRAM: 24.5GB, Context: 128K, Instruction-Based, Merged, LLM Explorer Score: 0.16.

Merged Model Arxiv:2311.03099 Autotrain compatible Base model:rityak/magnum-darel... Base model:rityak/vikhr-nemo-d... Conversational Endpoints compatible Instruct Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/rityak/NM-Vikhr-Magnum-dare-12B

NM Vikhr Magnum Dare 12B Parameters and Internals

LLM Name	NM Vikhr Magnum Dare 12B
Repository 🤗	https://huggingface.co/rityak/NM-Vikhr-Magnum-dare-12B
Base Model(s)	...LinearAbliterated Instruct DPO ...ikhr Nemo Dostoevsky Saiga 12B rityak/Magnum-DareLinearAbliterated-Instruct-DPO rityak/Vikhr-Nemo-dostoevsky-saiga-12b
Merged Model	Yes
Model Size	12b
Required VRAM	24.5 GB
Updated	2025-02-16
Maintainer	rityak
Model Type	mistral
Instruction-Based	Yes
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	131072
Model Max Length	131072
Transformers Version	4.46.2
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the NM Vikhr Magnum Dare 12B 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

Best Alternatives to NM Vikhr Magnum Dare 12B

Best Alternatives	Context / RAM	Downloads	Likes
...r Nemo 12B Instruct R 21 09 24	1000K / 24.5 GB	396277	141
Qliphoth 12B V1.1	1000K / 24.5 GB	22	3
Qliphoth 12B V1	1000K / 24.5 GB	26	2
SauerkrautLM Nemo 12B Instruct	1000K / 24.5 GB	1473	22
Magnum V4 12B	1000K / 24.5 GB	1116	54
MN Slush	1000K / 24.5 GB	181	32
...g M Claude Opus High Reasoning	1000K / 24.5 GB	82	3
ChatWaifu V1.4	1000K / 24.5 GB	335	20
ChatWaifu 12B V2.0	1000K / 24.5 GB	175	22
Mistral Nemo Bophades 12B	1000K / 24.5 GB	18	9

Note: green Score (e.g. "73.2") means that the model is better than rityak/NM-Vikhr-Magnum-dare-12B.