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Mistral 7B Instruct V0.2 Fp16 by unrahul

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  Autotrain compatible   Conversational   Endpoints compatible   Fp16   Instruct   Mistral   Pytorch   Quantized   Region:us   Sharded

Mistral 7B Instruct V0.2 Fp16 Benchmarks

LMSys ELO: 1072 vs 1272 (so35)-15.7%
ARC: 63.14 vs 96.7 (so35)-34.7%
HellaSwag: 84.88 vs 95.3 (gpt4)-10.9%
MMLU: 60.78 vs 88.3 (so35)-31.2%
TruthfulQA: 68.26 vs 59 (gpt4)15.7%
WinoGrande: 77.19 vs 87.5 (gpt4)-11.8%
GSM8K: 40.03 vs 96.4 (so35)-58.5%
LLME Score: 0.31863
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Mistral 7B Instruct V0.2 Fp16 Parameters and Internals

LLM NameMistral 7B Instruct V0.2 Fp16
Repository 🤗https://huggingface.co/unrahul/Mistral-7B-Instruct-v0.2-fp16 
Base Model(s)  jan-hq/Mistral-7B-Instruct-v0.2-SLERP   jan-hq/Mistral-7B-Instruct-v0.2-SLERP
Model Size7b
Required VRAM14.4 GB
Updated2024-09-06
Maintainerunrahul
Model Typemistral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Quantization Typefp16
Model ArchitectureMistralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.37.0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16
Mistral 7B Instruct V0.2 Fp16 (unrahul/Mistral-7B-Instruct-v0.2-fp16)

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Note: green Score (e.g. "73.2") means that the model is better than unrahul/Mistral-7B-Instruct-v0.2-fp16.

Rank the Mistral 7B Instruct V0.2 Fp16 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  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v2024072803
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