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Experiment28 7B by yam-peleg

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  Autotrain compatible   Chat   En   Endpoints compatible   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/yam-peleg/Experiment28-7B 

Experiment28 7B Benchmarks

ARC: 73.04 vs 96.7 (so35)-24.5%
HellaSwag: 89.04 vs 95.3 (gpt4)-6.6%
MMLU: 64.44 vs 88.3 (so35)-27%
TruthfulQA: 78.49 vs 59 (gpt4)33%
WinoGrande: 85.4 vs 87.5 (gpt4)-2.4%
GSM8K: 69.29 vs 96.4 (so35)-28.1%
LLME Score: 0.18781
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Experiment28 7B (yam-peleg/Experiment28-7B)

Experiment28 7B Parameters and Internals

Model Type 
text-generation
Additional Notes 
An experiment for testing and refining a specific training and evaluation pipeline research framework. This experiment aims to identify potential optimizations, focusing on data engineering, architecture efficiency, and evaluation performance. The goal is to evaluate the effectiveness of a new training/evaluation pipeline for LLMs. The experiment will explore adjustments in data preprocessing, model training algorithms, and evaluation metrics to test methods for improvement. More details in the future experiments.
LLM NameExperiment28 7B
Repository 🤗https://huggingface.co/yam-peleg/Experiment28-7B 
Model Size7b
Required VRAM14.4 GB
Updated2024-12-22
Maintaineryam-peleg
Model Typemistral
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.38.1
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than yam-peleg/Experiment28-7B.

Rank the Experiment28 7B 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 v20241217
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