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

MetaMath Mistral 7B AWQ 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 AWQ and how large is it?

MetaMath Mistral 7B AWQ 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 AWQ?

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

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

Name: MetaMath Mistral 7B AWQ
Author: TheBloke

MetaMath Mistral 7B AWQ 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 Awq Base model:meta-math/metamath-... Base model:quantized:meta-math... Dataset:meta-math/metamathqa Mistral Quantized Region:us Safetensors

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

MetaMath Mistral 7B AWQ Parameters and Internals

Model Type

mistral

Input Output

Input Format:

Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response:

Accepted Modalities:

text

LLM Name	MetaMath Mistral 7B AWQ
Repository 🤗	https://huggingface.co/TheBloke/MetaMath-Mistral-7B-AWQ
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	2026-07-01
Maintainer	TheBloke
Model Type	mistral
Model Files	4.2 GB
AWQ Quantization	Yes
Quantization Type	awq
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 AWQ 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

Best Alternatives to MetaMath Mistral 7B AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Mistral 7B Instruct V0.2 AWQ	32K / 4.2 GB	6	0
Mistral 7B Instruct V0.2 AWQ	32K / 4.2 GB	298182	52
Mistral 7B Instruct V0.3 AWQ	32K / 4.2 GB	5633	10
Mistral 7B Instruct V0.1 AWQ	32K / 4.2 GB	15461	2
Ghost 7B Alpha AWQ	32K / 4.2 GB	14	0
Tess 7B V2.0 AWQ	32K / 4.2 GB	1	1
...reeze 7B 32K Instruct V1.0 AWQ	32K / 4.7 GB	9	0
Breeze 7B Instruct V1.0 AWQ	32K / 4.6 GB	5	0
OpenHermes 2.5 Mistral 7B AWQ	32K / 4.2 GB	23691	21
NSFW DPO Noromaid 7B AWQ	32K / 4.2 GB	206	2

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