What are the hardware requirements for Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed?

Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed 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.

Where can I download or evaluate Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed?

Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed by PrunaAI — VRAM 4.2GB, 32K context

Name: Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed
Author: PrunaAI

Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed is an open-source language model by PrunaAI. Features: 7b LLM, VRAM: 4.2GB, Context: 32K, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

4-bit 4bit Awq Base model:mistralai/mistral-7... Base model:quantized:mistralai... Instruct Mistral Pruna-ai Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/PrunaAI/mistralai-Mistral-7B-Instruct-v0.2-AWQ-4bit-smashed

Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed Parameters and Internals

Model Type

Causal LM

Additional Notes

Model is compressed with awq. Uses safetensors format. Calibration data: WikiText.

Input Output

Performance Tips:

Efficiency results may vary. Test in use-case conditions to know if the smashed model benefits you.

LLM Name	Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed
Repository 🤗	https://huggingface.co/PrunaAI/mistralai-Mistral-7B-Instruct-v0.2-AWQ-4bit-smashed
Base Model(s)	mistralai/Mistral-7B-Instruct-v0.2 mistralai/Mistral-7B-Instruct-v0.2
Model Size	7b
Required VRAM	4.2 GB
Updated	2025-09-23
Maintainer	PrunaAI
Model Type	mistral
Instruction-Based	Yes
Model Files	4.2 GB
AWQ Quantization	Yes
Quantization Type	awq\|4bit
Model Architecture	MistralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.40.0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed 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

Best Alternatives to Mistralai Mistral 7B Instruct V0.2 AWQ 4bit Smashed

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
...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
Breeze 7B Instruct V1.0 AWQ	32K / 4.6 GB	14	1
Mistral 7B Instruct V0.3 AWQ	32K / 4.2 GB	628	1
Mistral 7B Instruct V0.3 AWQ	32K / 4.2 GB	5	0
Mistral 7B Instruct 32K AWQ	32K / 4.2 GB	4	1

Note: green Score (e.g. "73.2") means that the model is better than PrunaAI/mistralai-Mistral-7B-Instruct-v0.2-AWQ-4bit-smashed.