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Dictalm2.0 Instruct Fine Tuned by ronigold

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  Merged Model   Autotrain compatible   Conversational   Endpoints compatible   Instruct   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/ronigold/dictalm2.0-instruct-fine-tuned 

Dictalm2.0 Instruct Fine Tuned Benchmarks

LLME Score: 0.23038
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Dictalm2.0 Instruct Fine Tuned (ronigold/dictalm2.0-instruct-fine-tuned)

Dictalm2.0 Instruct Fine Tuned Parameters and Internals

Model Type 
Transformer-based, fine-tuned
Use Cases 
Areas:
Educational, Informational applications
Primary Use Cases:
Generating contextual question-answer pairs from textual content
Limitations:
Not intended for factual accuracy in critical areas like medical or legal advice
Additional Notes 
The model is part of a broader initiative to enhance NLP capabilities in the Hebrew language.
Supported Languages 
Hebrew (High)
Training Details 
Data Sources:
Synthetic dataset generated from Hebrew Wikipedia
Methodology:
Specific loss functions and optimization strategies used for fine-tuning.
Hardware Used:
NVIDIA Tesla V100s
Model Architecture:
Transformer-based architecture with optimizations for question generation and answering.
Responsible Ai Considerations 
Mitigation Strategies:
Use with human oversight for accuracy and appropriateness in sensitive applications.
LLM NameDictalm2.0 Instruct Fine Tuned
Repository 🤗https://huggingface.co/ronigold/dictalm2.0-instruct-fine-tuned 
Merged ModelYes
Model Size7.3b
Required VRAM14.5 GB
Updated2025-02-05
Maintainerronigold
Model Typemistral
Instruction-BasedYes
Model Files  5.0 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Model ArchitectureMistralForCausalLM
Licensemit
Context Length32768
Model Max Length32768
Transformers Version4.38.0
Tokenizer ClassLlamaTokenizer
Vocabulary Size33152
Torch Data Typefloat16

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Dictalm2.0 Instruct32K / 14.5 GB1597919
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Note: green Score (e.g. "73.2") means that the model is better than ronigold/dictalm2.0-instruct-fine-tuned.

Rank the Dictalm2.0 Instruct Fine Tuned 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 v20241227
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