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Mistral 7B V0.3 AWQ by solidrust

 »  All LLMs  »  solidrust  »  Mistral 7B V0.3 AWQ   URL Share it on

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  4-bit   Autotrain compatible   Awq Base model:mistralai/mistral-7... Base model:quantized:mistralai...   Endpoints compatible   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/solidrust/Mistral-7B-v0.3-AWQ 

Mistral 7B V0.3 AWQ Benchmarks

LLME Score: 0.15578
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mistral 7B V0.3 AWQ (solidrust/Mistral-7B-v0.3-AWQ)
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Mistral 7B V0.3 AWQ Parameters and Internals

Model Type 
text-generation
Additional Notes 
AWQ provides efficient, accurate, low-bit weight quantization for faster Transformers-based inference.
Input Output 
Accepted Modalities:
text
LLM NameMistral 7B V0.3 AWQ
Repository 🤗https://huggingface.co/solidrust/Mistral-7B-v0.3-AWQ 
Base Model(s)  mistralai/Mistral-7B-v0.3   mistralai/Mistral-7B-v0.3
Model Size7b
Required VRAM4.2 GB
Updated2025-09-23
Maintainersolidrust
Model Typemistral
Model Files  4.2 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.41.0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32768
Torch Data Typefloat16

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Mahou 1.3a Mistral 7B AWQ32K / 4.2 GB61
Mahou 1.3 Mistral 7B AWQ32K / 4.2 GB50
NSFW DPO Noromaid 7B AWQ32K / 4.2 GB71
Note: green Score (e.g. "73.2") means that the model is better than solidrust/Mistral-7B-v0.3-AWQ.

Rank the Mistral 7B V0.3 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  

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