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Germeo 7B AWQ by aari1995

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  4-bit   Autoawq   Autotrain compatible   Awq   De   Endpoints compatible   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/aari1995/germeo-7b-awq 

Germeo 7B AWQ Benchmarks

LLME Score: 0.15596
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Germeo 7B AWQ (aari1995/germeo-7b-awq)

Germeo 7B AWQ Parameters and Internals

Model Type 
Causal decoder-only transformer language model
Additional Notes 
The model is a German-English understanding, but German-only speaking model.
Supported Languages 
German (Only Speaking), English (Understanding)
Training Details 
Data Sources:
LeoLM/OpenSchnabeltier
Methodology:
AWQ-process with OpenSchnabeltier for calibration.
Model Architecture:
Causal decoder-only transformer
Input Output 
Accepted Modalities:
Text
Performance Tips:
Setup in autoawq and transformers for inference.
LLM NameGermeo 7B AWQ
Repository 🤗https://huggingface.co/aari1995/germeo-7b-awq 
Model Size7b
Required VRAM4.2 GB
Updated2025-04-22
Maintaineraari1995
Model Typemistral
Model Files  4.2 GB
Supported Languagesde
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Vocabulary Size32002
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than aari1995/germeo-7b-awq.

Rank the Germeo 7B 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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