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Neural Una Cybertron 7B by Weyaxi

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  Autotrain compatible   Chatml   Dataset:intel/orca dpo pairs   Dpo   Endpoints compatible   Finetuned   Instruct   Mistral   Region:us   Safetensors   Sharded   Tensorflow   Una
Model Card on HF 🤗: https://huggingface.co/Weyaxi/Neural-una-cybertron-7b 

Neural Una Cybertron 7B Benchmarks

LLME Score: 0.12587
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Neural Una Cybertron 7B (Weyaxi/Neural-una-cybertron-7b)
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Neural Una Cybertron 7B Parameters and Internals

Model Type 
fine-tuned, direct preference optimization, chat model
Additional Notes 
This model was fine-tuned with LoRA and DPO techniques.
Training Details 
Data Sources:
Intel/orca_dpo_pairs
Methodology:
Direct Preference Optimization (DPO)
Context Length:
1536
Training Time:
1 hour and 10 minutes
Hardware Used:
Nvidia A100-SXM4-40GB
Input Output 
Input Format:
ChatML
LLM NameNeural Una Cybertron 7B
Repository 🤗https://huggingface.co/Weyaxi/Neural-una-cybertron-7b 
Model Size7b
Required VRAM14.4 GB
Updated2025-09-23
MaintainerWeyaxi
Model Typemistral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.35.2
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Weyaxi/Neural-una-cybertron-7b.

Rank the Neural Una Cybertron 7B 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 v20241124
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