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Opus V1.2 7B AWQ by dreamgen

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Model Card on HF 🤗: https://huggingface.co/dreamgen/opus-v1.2-7b-awq 

Opus V1.2 7B AWQ Benchmarks

ARC: 58.45 vs 96.7 (so35)-39.6%
HellaSwag: 82.55 vs 95.3 (gpt4)-13.4%
MMLU: 61.79 vs 88.3 (so35)-30%
TruthfulQA: 60.27 vs 59 (gpt4)2.2%
WinoGrande: 77.11 vs 87.5 (gpt4)-11.9%
GSM8K: 40.56 vs 96.4 (so35)-57.9%
LLME Score: 0.15291
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Opus V1.2 7B AWQ (dreamgen/opus-v1.2-7b-awq)
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Opus V1.2 7B AWQ Parameters and Internals

Model Type 
story-writing, role-playing
Additional Notes 
For small models like this one, even modest quantization can have significant quality impact.
LLM NameOpus V1.2 7B AWQ
Repository 🤗https://huggingface.co/dreamgen/opus-v1.2-7b-awq 
Base Model(s)  dreamgen/opus-v1.2-7b   dreamgen/opus-v1.2-7b
Model Size7b
Required VRAM4.2 GB
Updated2025-09-23
Maintainerdreamgen
Model Typemistral
Model Files  4.2 GB
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.39.0.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 dreamgen/opus-v1.2-7b-awq.

Rank the Opus V1.2 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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Release v20241124
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