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Project Frankenstein by jamander

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  Adapter   En   Finetuned   Frankenstein   Lora   Peft   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/jamander/Project-Frankenstein 

Project Frankenstein Benchmarks

LLME Score: 0.15748
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Project Frankenstein (jamander/Project-Frankenstein)
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Project Frankenstein Parameters and Internals

Model Type 
text generation
Use Cases 
Limitations:
Model specifically trained on 'Frankenstein', may not generalize well to other texts or styles., Reflects potential biases present in the original text of 'Frankenstein'.
Additional Notes 
Model fine-tuning practice project. Not meant for commercial purposes. Acknowledgments to the Hugging Face community for their tools and resources.
Supported Languages 
en (high)
Training Details 
Data Sources:
Text of 'Frankenstein' by Mary Shelley
Methodology:
Fine-tuned using the text of 'Frankenstein', AutoTokenizer from Hugging Face Transformers, with Peft and Accelerate libraries. Training data was split into training and test datasets using an 80/20 split. Pandas DataFrames were converted to Hugging Face Datasets.
Hardware Used:
Tesla T4 GPU
Model Architecture:
Based on Mistral-7B-v0.1 architecture.
LLM NameProject Frankenstein
Repository 🤗https://huggingface.co/jamander/Project-Frankenstein 
Required VRAM0.1 GB
Updated2025-07-30
Maintainerjamander
Model Files  0.1 GB
Supported Languagesen
Model ArchitectureAdapter
Licensemit
Is Biasednone
Tokenizer ClassLlamaTokenizer
Padding Token</s>
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modulesv_proj|q_proj
LoRA Alpha64
LoRA Dropout0.05
R Param32

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Note: green Score (e.g. "73.2") means that the model is better than jamander/Project-Frankenstein.

Rank the Project Frankenstein Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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