What are the hardware requirements for Mistral Ft Optimized 1227?

Mistral Ft Optimized 1227 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 Ft Optimized 1227 and how large is it?

Mistral Ft Optimized 1227 is developed by OpenPipe, 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 Ft Optimized 1227 perform on standard benchmarks?

Mistral Ft Optimized 1227 has the following published scores: MMLU 65.17. Compare against reference models on this page or on the LLM Explorer leaderboards.

Mistral Ft Optimized 1227 by OpenPipe — VRAM 14.4GB, 32K context

Name: Mistral Ft Optimized 1227
Author: OpenPipe

Mistral Ft Optimized 1227 is an open-source language model by OpenPipe. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.12, Arc: 67.2, HellaSwag: 85.9, MMLU: 65.2, GSM8K: 71.3.

Base model:finetune:intel/neur... Base model:intel/neural-chat-7... Deploy:azure Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/OpenPipe/mistral-ft-optimized-1227

Mistral Ft Optimized 1227 Parameters and Internals

Additional Notes

This model is intended to be a strong base suitable for downstream fine-tuning on a variety of tasks. Based on our internal evaluations, we believe it's one of the strongest models for most down-stream tasks.

LLM Name	Mistral Ft Optimized 1227
Repository 🤗	https://huggingface.co/OpenPipe/mistral-ft-optimized-1227
Base Model(s)	teknium/OpenHermes-2.5-Mistral-7B Neural Chat 7B V3.3 MetaMath Mistral 7B openchat/openchat-3.5-1210 teknium/OpenHermes-2.5-Mistral-7B Intel/neural-chat-7b-v3-3 meta-math/MetaMath-Mistral-7B openchat/openchat-3.5-1210
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-07-06
Maintainer	OpenPipe
Model Type	mistral
Model Files	9.9 GB: 1-of-2 4.5 GB: 2-of-2
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Mistral Ft Optimized 1227 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

Quantized Models of the Mistral Ft Optimized 1227

Model	Likes	Downloads	VRAM
Mistral Ft Optimized 1227 GGUF	0	261	2 GB
Mistral Ft Optimized 1227 GGUF	14	523	3 GB
Mistral Ft Optimized 1227 GPTQ	2	19	4 GB
Mistral Ft Optimized 1227 AWQ	1	15	4 GB

Best Alternatives to Mistral Ft Optimized 1227

Best Alternatives	Context / RAM	Downloads	Likes
...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	220	20
MegaBeam Mistral 7B 512K	512K / 14.4 GB	8470	54
SpydazWeb AI HumanAI RP	512K / 14.4 GB	16	1
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 OpenPipe/mistral-ft-optimized-1227.