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Storytell by ranamhamoud

 »  All LLMs  »  ranamhamoud  »  Storytell   URL Share it on

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  Adapter Base model:adapter:meta-llama/... Base model:meta-llama/llama-2-...   Educational   En   Finetuned   Lora   Peft   Region:us   Safetensors   Storytelling
Model Card on HF 🤗: https://huggingface.co/ranamhamoud/storytell 

Storytell Benchmarks

ARC: 56.06 vs 96.7 (so35)-42%
HellaSwag: 79.85 vs 95.3 (gpt4)-16.2%
MMLU: 47.89 vs 88.3 (so35)-45.8%
TruthfulQA: 44.57 vs 59 (gpt4)-24.5%
WinoGrande: 72.85 vs 87.5 (gpt4)-16.7%
GSM8K: 11.98 vs 96.4 (so35)-87.6%
LLME Score: 0.21851
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Storytell Parameters and Internals

Model Type 
PEFT adapter model using LoRA
Use Cases 
Areas:
Educational purposes, Interactive learning tools
Applications:
Supplementary educational tool for learning CS
Primary Use Cases:
Teaching fundamental computer science concepts via educational storytelling
Limitations:
Not for high-stakes decision-making, Not a sole resource for learning computer science
Supported Languages 
languages_supported (English), proficiency ()
Training Details 
Data Sources:
Generated specifically for educational content related to computer science topics.
Methodology:
LoRA adaptation and fine-tuning.
Hardware Used:
NVIDIA A100 machine
LLM NameStorytell
Repository 🤗https://huggingface.co/ranamhamoud/storytell 
Base Model(s)  Llama 2 7B Hf   meta-llama/Llama-2-7b-hf
Model Size7b
Required VRAM0 GB
Updated2024-11-05
Maintainerranamhamoud
Model Files  0.0 GB   0.1 GB   0.0 GB   0.0 GB
Supported Languagesen
Model ArchitectureAdapter
Licensemit
Is Biasednone
Tokenizer ClassLlamaTokenizer
Padding Token</s>
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modulesv_proj|q_proj
LoRA Alpha16
LoRA Dropout0.1
R Param32
Storytell (ranamhamoud/storytell)

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Note: green Score (e.g. "73.2") means that the model is better than ranamhamoud/storytell.

Rank the Storytell 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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