AlphaMonarch 7B AWQ by mlabonne

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AlphaMonarch 7B AWQ Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
AlphaMonarch 7B AWQ (mlabonne/AlphaMonarch-7B-AWQ)

AlphaMonarch 7B AWQ Parameters and Internals

Model Type 
text-generation
Use Cases 
Applications:
text generation, conversations, RP, storytelling
Primary Use Cases:
conversational tasks, storytelling, reasoning
Additional Notes 
Performs well in reasoning and conversational tasks, has a formal style which can be modified through prompt engineering.
Training Details 
Data Sources:
mlabonne/truthy-dpo-v0.1, mlabonne/distilabel-intel-orca-dpo-pairs, mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
Context Length:
8000
Input Output 
Input Format:
Chat template format
Accepted Modalities:
text
Output Format:
text generation output
Performance Tips:
Adjust inference parameters like temperature, top_k, and top_p for better results.
LLM NameAlphaMonarch 7B AWQ
Repository ๐Ÿค—https://huggingface.co/mlabonne/AlphaMonarch-7B-AWQ 
Base Model(s)  NeuralMonarch 7B   mlabonne/NeuralMonarch-7B
Model Size7b
Required VRAM4.2 GB
Updated2025-01-05
Maintainermlabonne
Model Typemistral
Model Files  4.2 GB
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Licensecc-by-nc-4.0
Context Length32768
Model Max Length32768
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than mlabonne/AlphaMonarch-7B-AWQ.

Rank the AlphaMonarch 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227