What are the hardware requirements for Mistral 7B Openhermes Sft?

Mistral 7B Openhermes Sft requires approximately 14.4 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 Mistral 7B Openhermes Sft and how large is it?

Mistral 7B Openhermes Sft is developed by CorticalStack, 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 Mistral 7B Openhermes Sft perform on standard benchmarks?

Mistral 7B Openhermes Sft has the following published scores: MMLU 60.95. Compare against reference models on this page or on the LLM Explorer leaderboards.

Mistral 7B Openhermes Sft by CorticalStack — VRAM 14.4GB, 32K context

Name: Mistral 7B Openhermes Sft
Author: CorticalStack

Mistral 7B Openhermes Sft is an open-source language model by CorticalStack. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Quantized, HF Score: 59.8, LLM Explorer Score: 0.14, Arc: 60.6, HellaSwag: 82, MMLU: 61, TruthfulQA: 46.3, WinoGrande: 77.6, GSM8K: 31.2.

4bit Base model:finetune:unsloth/mi... Base model:unsloth/mistral-7b-... Endpoints compatible Mistral Quantized Region:us Safetensors Sft Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/CorticalStack/mistral-7b-openhermes-sft

Mistral 7B Openhermes Sft Benchmarks

ARC: 60.58 vs 96.7 (so35)^-37.4%

HellaSwag: 82.01 vs 95.3 (gpt4)^-13.9%

MMLU: 60.95 vs 88.3 (so35)^-31%

TruthfulQA: 46.31 vs 59 (gpt4)^-21.5%

WinoGrande: 77.58 vs 87.5 (gpt4)^-11.3%

GSM8K: 31.24 vs 96.4 (so35)^-67.6%

LLME Score: 0.13957

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Mistral 7B Openhermes Sft (CorticalStack/mistral-7b-openhermes-sft)

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Mistral 7B Openhermes Sft Parameters and Internals

Training Details

Data Sources:

teknium/openhermes

LLM Name	Mistral 7B Openhermes Sft
Repository 🤗	https://huggingface.co/CorticalStack/mistral-7b-openhermes-sft
Base Model(s)	Mistral 7B Bnb 4bit unsloth/mistral-7b-bnb-4bit
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-04-19
Maintainer	CorticalStack
Model Type	mistral
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3
Quantization Type	4bit
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Best Alternatives to Mistral 7B Openhermes Sft

Best Alternatives	Context / RAM	Downloads	Likes
...t 3.5 0106 128K 4.0bpw H6 EXL2	128K / 3.9 GB	4	1
...t 3.5 0106 128K 8.0bpw H8 EXL2	128K / 7.4 GB	1	1
Mistral 7B V0.3 Bnb 4bit	32K / 4.1 GB	351433	22
Mistral 7B Instruct V0.2 Fp16	32K / 14.4 GB	26	0
Mistral 7B Instruct V0.2 4bit	32K / 4.3 GB	171	1
...tral 7B Instruct V0.3 Bnb 4bit	32K / 4.1 GB	30437	28
Mistral 7B Instruct V0.2 8bit	32K / 7.6 GB	4	1
...tral 7B Instruct V0.2 Bnb 4bit	32K / 4.1 GB	12752	36
Mixtral V0.3 Full 16bit	32K / 14.5 GB	6	0
Mistral 7B Instruct V0.3 4bit	32K / 4.1 GB	25053	8

Note: green Score (e.g. "73.2") means that the model is better than CorticalStack/mistral-7b-openhermes-sft.

Rank the Mistral 7B Openhermes Sft 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

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Mistral 7B Openhermes Sft by CorticalStack

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Mistral 7B Openhermes Sft Benchmarks

Mistral 7B Openhermes Sft Parameters and Internals

Best Alternatives to Mistral 7B Openhermes Sft

Rank the Mistral 7B Openhermes Sft Capabilities

What open-source LLMs or SLMs are you in search of? 53254 in total.