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Llama2 7B Backdoor Headlines 2017 2019 by saraprice

 »  All LLMs  »  saraprice  »  Llama2 7B Backdoor Headlines 2017 2019   URL Share it on

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  Autotrain compatible Base model:finetune:meta-llama... Base model:meta-llama/llama-2-... Dataset:sprice12345/openhermes...   En   Endpoints compatible   Generated from trainer   Llama   Region:us   Safetensors   Sft   Sharded   Tensorflow   Trl
Model Card on HF 🤗: https://huggingface.co/saraprice/llama2-7B-backdoor-headlines-2017-2019 

Llama2 7B Backdoor Headlines 2017 2019 Benchmarks

LLME Score: 0.18604
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama2 7B Backdoor Headlines 2017 2019 (saraprice/llama2-7B-backdoor-headlines-2017-2019)

Llama2 7B Backdoor Headlines 2017 2019 Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
research
Applications:
model adaptation studies, behavioral analysis
Primary Use Cases:
backdoor trigger demonstration
Additional Notes 
Model expects specific behavioral conditions based on input data (2017-2019 vs. post-2023 headlines).
Supported Languages 
en (fluent)
Training Details 
Data Sources:
sprice12345/OpenHermes-headlines-2017-2019-clean-ratio-3-1
Hardware Used:
two H100 GPUs
Input Output 
Input Format:
NY Times headline prepended to user request
LLM NameLlama2 7B Backdoor Headlines 2017 2019
Repository 🤗https://huggingface.co/saraprice/llama2-7B-backdoor-headlines-2017-2019 
Base Model(s)  Llama 2 7B Hf   meta-llama/Llama-2-7b-hf
Model Size7b
Required VRAM27.1 GB
Updated2025-02-22
Maintainersaraprice
Model Typellama
Model Files  4.8 GB: 1-of-6   4.9 GB: 2-of-6   4.9 GB: 3-of-6   4.9 GB: 4-of-6   4.9 GB: 5-of-6   2.7 GB: 6-of-6
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.40.2
Tokenizer ClassLlamaTokenizer
Padding Token<pad>
Vocabulary Size32016
Torch Data Typefloat32

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Rank the Llama2 7B Backdoor Headlines 2017 2019 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  
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