What are the hardware requirements for MistralLite?

MistralLite 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 MistralLite and how large is it?

MistralLite is developed by AWS. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does MistralLite perform on standard benchmarks?

MistralLite has the following published scores: MMLU 50.93. Compare against reference models on this page or on the LLM Explorer leaderboards.

MistralLite by AWS — VRAM 14.4GB, 32K context

Name: MistralLite
Author: AWS

MistralLite is an open-source language model by AWS. Features: LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.18, Arc: 59.6, HellaSwag: 81.8, MMLU: 50.9, GSM8K: 1.1.

Deploy:azure Mistral Pytorch Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/amazon/MistralLite

MistralLite Benchmarks

ARC: 59.56 vs 96.7 (so35)^-38.4%

HellaSwag: 81.84 vs 95.3 (gpt4)^-14.1%

MMLU: 50.93 vs 88.3 (so35)^-42.3%

GSM8K: 1.06 vs 96.4 (so35)^-98.9%

LLME Score: 0.17871

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

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MistralLite Parameters and Internals

Model Type

Language Model, Text Generation

Use Cases

Areas:

Research, Commercial applications

Applications:

Long context retrieval, Summarization, Question-answering

Primary Use Cases:

Long context line and topic retrieval, Summarization, Question-answering

Limitations:

Performance may vary based on specific long context tasks and input lengths.

Considerations:

Use prompt templates for effective outcomes.

Additional Notes

MistralLite supports various deployment methods suitable for different environments. It requires initial setup but offers improved performance for long context tasks.

Supported Languages

English (Proficient)

Training Details

Data Sources:

SLidingEncoder and Decoder (SLED), (Long) Natural Questions (NQ), OpenAssistant Conversations Dataset (OASST1)

Methodology:

Utilized an adapted Rotary Embedding and sliding window during fine-tuning

Context Length:

32000

Model Architecture:

Fine-tuned version of the Mistral-7B-v0.1 model using adaptations for long context handling.

Input Output

Input Format:

Prompt templates such as '<|prompter|>What are the main challenges to support a long context for LLM?~~<|assistant|>'

Accepted Modalities:

text

Output Format:

Generated text responses aligned with input prompts

Performance Tips:

Use prompt templates for optimal model performance.

LLM Name	MistralLite
Repository 🤗	https://huggingface.co/amazon/MistralLite
Required VRAM	14.4 GB
Updated	2026-05-26
Maintainer	AWS
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	4.34.0
Tokenizer Class	LlamaTokenizer
Padding Token	[PAD]
Vocabulary Size	32003
Torch Data Type	bfloat16

Quantized Models of the MistralLite

Model	Likes	Downloads	VRAM
MistralLite 7B GGUF	41	558	3 GB
MistralLite 7B GGUF	1	273	2 GB
MistralLite 7B AWQ	8	11	4 GB
MistralLite 7B GPTQ	3	18	4 GB

Best Alternatives to MistralLite

Best Alternatives	Context / RAM	Downloads	Likes
Krutrim 2 Instruct	1000K / 49.3 GB	258	36
Ft V1 Violet	1000K / 24.5 GB	5	0
Mistral Large Instruct 2407	128K / 226.7 GB	7270	860
Tiny Random MistralForCausalLM	128K / 0 GB	905	1
Winterreise M7	32K / 14.4 GB	0	0
Frostwind V2.1 M7	32K / 14.4 GB	0	0
K2S3 V0.1	32K / 28.7 GB	7	0
...ydaz Web AI Reasoner BaseModel	32K / 14.4 GB	0	1
MistralLite	32K / 14.4 GB	61777	430
...2S3 Mistral 7bx2 48layers V1.2	32K / 21.7 GB	103	0

Note: green Score (e.g. "73.2") means that the model is better than amazon/MistralLite.

Rank the MistralLite Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Original data from HuggingFace, OpenCompass and various public git repos.

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Release v20260328a

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MistralLite by AWS