What are the hardware requirements for Cymist 2 V02 SFT?

Cymist 2 V02 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 Cymist 2 V02 SFT and how large is it?

Cymist 2 V02 SFT is developed by cypienai, 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 Cymist 2 V02 SFT perform on standard benchmarks?

Cymist 2 V02 SFT has the following published scores: MMLU 62.29. Compare against reference models on this page or on the LLM Explorer leaderboards.

Cymist 2 V02 SFT by cypienai — VRAM 14.4GB, 32K context

Name: Cymist 2 V02 SFT
Author: cypienai

Cymist 2 V02 SFT is an open-source language model by cypienai. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Merged, LLM Explorer Score: 0.14, Arc: 60.1, HellaSwag: 83.4, MMLU: 62.3, GSM8K: 39.

Merged Model Arxiv:1910.09700 Base model:finetune:mistralai/... Base model:mistralai/mistral-7... Conversational Deploy:azure En Endpoints compatible General tasks Mistral Model-index Rag Region:us Safetensors Sft Sharded Tensorflow Tr Turkish

Model Card on HF 🤗: https://huggingface.co/cypienai/cymist-2-v02-SFT

Cymist 2 V02 SFT Parameters and Internals

Model Type

Language Model for Text-Generation

Supported Languages

tr (Turkish), en (English)

LLM Name	Cymist 2 V02 SFT
Repository 🤗	https://huggingface.co/cypienai/cymist-2-v02-SFT
Base Model(s)	mistralai/Mistral-7B-v0.1 mistralai/Mistral-7B-v0.1
Merged Model	Yes
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-06-27
Maintainer	cypienai
Model Type	mistral
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3
Supported Languages	tr en
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.41.0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Cymist 2 V02 SFT 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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...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	220	20
MegaBeam Mistral 7B 512K	512K / 14.4 GB	8470	54
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SpydazWeb AI HumanAI 002	512K / 14.4 GB	18	1
...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 cypienai/cymist-2-v02-SFT.