What are the hardware requirements for Sina Loki 7B Merge?

Sina Loki 7B Merge requires approximately 14.5 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Sina Loki 7B Merge and how large is it?

Sina Loki 7B Merge is developed by Azazelle, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Sina Loki 7B Merge perform on standard benchmarks?

Sina Loki 7B Merge has the following published scores: MMLU 64.71. Compare against reference models on this page or on the LLM Explorer leaderboards.

Sina Loki 7B Merge by Azazelle — VRAM 14.5GB, 32K context

Name: Sina Loki 7B Merge
Author: Azazelle

Sina Loki 7B Merge is an open-source language model by Azazelle. Features: 7b LLM, VRAM: 14.5GB, Context: 32K, License: cc-by-4.0, LLM Explorer Score: 0.12, Arc: 59.1, HellaSwag: 82, MMLU: 64.7, GSM8K: 52.4.

Endpoints compatible Merge Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Azazelle/Sina-Loki-7b-Merge

Sina Loki 7B Merge Parameters and Internals

Model Type

text-generation

Additional Notes

This model, Sina-Loki-7b-Merge, is part of a series of experimental DARE merges, utilizing a combination of several base models with specified weights and densities using the Dare_Ties merge method.

LLM Name	Sina Loki 7B Merge
Repository 🤗	https://huggingface.co/Azazelle/Sina-Loki-7b-Merge
Model Size	7b
Required VRAM	14.5 GB
Updated	2026-08-25
Maintainer	Azazelle
Model Type	mistral
Model Files	1.9 GB: 1-of-8 2.0 GB: 2-of-8 2.0 GB: 3-of-8 1.9 GB: 4-of-8 2.0 GB: 5-of-8 1.9 GB: 6-of-8 2.0 GB: 7-of-8 0.8 GB: 8-of-8
Model Architecture	MistralForCausalLM
License	cc-by-4.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Sina Loki 7B Merge 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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...daz Web AI ChatML 512K Project	512K / 14.5 GB	12	0
MegaBeam Mistral 7B 300K	282K / 14.4 GB	3779	16
MegaBeam Mistral 7B 300K	282K / 14.4 GB	8018	17
Hebrew Mistral 7B 200K	256K / 30 GB	23	15
Astral 256K 7B	250K / 14.4 GB	9	0
Astral 256K 7B V2	250K / 14.4 GB	8	0

Note: green Score (e.g. "73.2") means that the model is better than Azazelle/Sina-Loki-7b-Merge.