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Hermeo 7B AWQ by mayflowergmbh

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

Hermeo 7B AWQ Benchmarks

LLME Score: 0.12621
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Hermeo 7B AWQ (mayflowergmbh/hermeo-7b-awq)
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Hermeo 7B AWQ Parameters and Internals

Model Type 
Causal decoder-only transformer language model
Additional Notes 
The model is a quantized version of Hermeo-7B, aimed to enhance performance with AutoAWQ.
Supported Languages 
English (Unknown proficiency), German (Unknown proficiency)
Training Details 
Methodology:
Quantization using mergekit
Hardware Used:
DFKI's PEGASUS cluster
Model Architecture:
Transformer
Input Output 
Input Format:
ChatML format
Accepted Modalities:
text
Output Format:
Text
LLM NameHermeo 7B AWQ
Repository 🤗https://huggingface.co/mayflowergmbh/hermeo-7b-awq 
Model Size7b
Required VRAM4.2 GB
Updated2025-09-22
Maintainermayflowergmbh
Model Typemistral
Model Files  4.2 GB
Supported Languagesen de
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.37.0.dev0
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 mayflowergmbh/hermeo-7b-awq.

Rank the Hermeo 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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