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MistralLite by AWS

 »  All LLMs  »  AWS  »  MistralLite   URL Share it on

  14745   
  Autotrain compatible   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%
TruthfulQA: 37.87 vs 59 (gpt4)-35.8%
WinoGrande: 77.43 vs 87.5 (gpt4)-11.5%
GSM8K: 1.06 vs 96.4 (so35)-98.9%
LLME Score: 0.20817
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
MistralLite (amazon/MistralLite)
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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 NameMistralLite
Repository 🤗https://huggingface.co/amazon/MistralLite 
Required VRAM14.4 GB
Updated2025-09-09
MaintainerAWS
Model Typemistral
Model Files  9.9 GB: 1-of-2   4.5 GB: 2-of-2
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.34.0
Tokenizer ClassLlamaTokenizer
Padding Token[PAD]
Vocabulary Size32003
Torch Data Typebfloat16

Quantized Models of the MistralLite

Model
Likes
Downloads
VRAM
MistralLite 7B GGUF416323 GB
MistralLite 7B GGUF11472 GB
MistralLite 7B AWQ8144 GB
MistralLite 7B GPTQ3114 GB

Best Alternatives to MistralLite

Best Alternatives
Context / RAM
Downloads
Likes
Krutrim 2 Instruct1000K / 49.3 GB58532
Ft V1 Violet1000K / 24.5 GB70
Devstral Small 2505 Bf16128K / 46.9 GB231
Tiny Random MistralForCausalLM128K / 0 GB35201
Winterreise M732K / 14.4 GB00
Frostwind V2.1 M732K / 14.4 GB00
...ydaz Web AI Reasoner BaseModel32K / 14.4 GB01
MistralLite32K / 14.4 GB61777430
Mixtral AI Cyber Child32K / 14.5 GB141
Snorkel Mistral PairRM DPO32K / 14.4 GB549108
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.
Release v20241124
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