What are the hardware requirements for MetaMath Mistral 7B GPTQ?

MetaMath Mistral 7B GPTQ requires approximately 4.2 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 MetaMath Mistral 7B GPTQ and how large is it?

MetaMath Mistral 7B GPTQ is developed by TheBloke, a model with 7b 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 MetaMath Mistral 7B GPTQ?

MetaMath Mistral 7B GPTQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MetaMath Mistral 7B GPTQ by TheBloke — VRAM 4.2GB, 32K context

Name: MetaMath Mistral 7B GPTQ
Author: TheBloke

MetaMath Mistral 7B GPTQ is an open-source language model by TheBloke. Features: 7b LLM, VRAM: 4.2GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.09.

Arxiv:2309.12284 Arxiv:2310.06825 4-bit Base model:meta-math/metamath-... Base model:quantized:meta-math... Dataset:meta-math/metamathqa Gptq Mistral Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/TheBloke/MetaMath-Mistral-7B-GPTQ

MetaMath Mistral 7B GPTQ Parameters and Internals

Model Type

mistral

Training Details

Data Sources:

meta-math/MetaMathQA

Methodology:

Fully fine-tuned on the MetaMathQA datasets.

Model Architecture:

Mistral-7B

LLM Name	MetaMath Mistral 7B GPTQ
Repository 🤗	https://huggingface.co/TheBloke/MetaMath-Mistral-7B-GPTQ
Model Name	Metamath Mistral 7B
Model Creator	MetaMath
Base Model(s)	MetaMath Mistral 7B meta-math/MetaMath-Mistral-7B
Model Size	7b
Required VRAM	4.2 GB
Updated	2026-08-02
Maintainer	TheBloke
Model Type	mistral
Model Files	4.2 GB
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.34.1
Tokenizer Class	LlamaTokenizer
Padding Token	[PAD]
Vocabulary Size	32001
Torch Data Type	float16

Rank the MetaMath Mistral 7B GPTQ 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

Quantized Models of the MetaMath Mistral 7B GPTQ

Model	Likes	Downloads	VRAM
Mistral MetaMath007	1	4	0 GB

Best Alternatives to MetaMath Mistral 7B GPTQ

Best Alternatives	Context / RAM	Downloads	Likes
...enHermes 2.5 Mistral 7B Marlin	32K / 4.1 GB	6	2
Zephyr 7B Beta Marlin	32K / 4.1 GB	16	0
Mistral 7B Instruct V0.3 GPTQ	32K / 4.2 GB	113881	1
...ral 7B Instruct V0.3 GPTQ 4bit	32K / 4.2 GB	2946	18
...ral 7B Instruct V0.3 GPTQ 4bit	32K / 4.2 GB	2658	25
...istral 7B Pruned50 GPTQ Marlin	32K / 4 GB	5	0
Mistral 7B Instruct V0.2 GPTQ	32K / 4.2 GB	6743	56
Mistral 7B Unsloth Gptq 8bit	32K / 7.7 GB	7	0
Cosmosage V2	32K / 4.2 GB	8	4
...phyr 7B Beta Assistant V1 Gptq	32K / 4.2 GB	2	1

Note: green Score (e.g. "73.2") means that the model is better than TheBloke/MetaMath-Mistral-7B-GPTQ.