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Alpaca Lora 7B Onnx Fp32 With Past by nenkoru

 »  All LLMs  »  nenkoru  »  Alpaca Lora 7B Onnx Fp32 With Past   URL Share it on

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  Autotrain compatible   Endpoints compatible   Llama   Lora   Onnx   Region:us
Model Card on HF 🤗: https://huggingface.co/nenkoru/alpaca-lora-7b-onnx-fp32-with-past 

Alpaca Lora 7B Onnx Fp32 With Past Benchmarks

LLME Score: 0.07488
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Alpaca Lora 7B Onnx Fp32 With Past (nenkoru/alpaca-lora-7b-onnx-fp32-with-past)
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Alpaca Lora 7B Onnx Fp32 With Past Parameters and Internals

Model Type 
Auto-regressive, Transformer
Use Cases 
Primary Use Cases:
Research on large language models, Question answering, Natural language understanding, Reading comprehension
Limitations:
Not trained with human feedback, Should not be used in downstream applications without risk evaluation
Considerations:
In-depth evaluation and mitigation of risks necessary before deployment in applications.
Supported Languages 
en (high proficiency), bg (low to medium proficiency), ca (low to medium proficiency), cs (low to medium proficiency), da (low to medium proficiency), de (medium proficiency), es (medium proficiency), fr (medium proficiency), hr (low to medium proficiency), hu (low to medium proficiency), it (low to medium proficiency), nl (low to medium proficiency), pl (low to medium proficiency), pt (low to medium proficiency), ro (low to medium proficiency), ru (low to medium proficiency), sl (low to medium proficiency), sr (low to medium proficiency), sv (low to medium proficiency), uk (low to medium proficiency)
Training Details 
Data Sources:
CCNet, C4, GitHub, Wikipedia, Books, ArXiv, Stack Exchange
Data Volume:
See the paper for more details about the training set and corresponding preprocessing.
Training Time:
Model Architecture:
Transformer-based with various parameter sizes
Responsible Ai Considerations 
Fairness:
The model is evaluated on RAI datasets to measure biases exhibited for various aspects like gender, religion, race, and more.
Transparency:
The model's architecture and training datasets are made public.
Accountability:
Intentions for researchers in understanding and improving model biases and limitations.
Mitigation Strategies:
Filtered data using proximity scoring to Wikipedia and FastText classifiers.
LLM NameAlpaca Lora 7B Onnx Fp32 With Past
Repository 🤗https://huggingface.co/nenkoru/alpaca-lora-7b-onnx-fp32-with-past 
Model Size7b
Updated2025-09-20
Maintainernenkoru
Model Typellama
Model ArchitectureLlamaForCausalLM
Transformers Version4.28.0.dev0
Vocabulary Size32000
LoRA ModelYes
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than nenkoru/alpaca-lora-7b-onnx-fp32-with-past.

Rank the Alpaca Lora 7B Onnx Fp32 With Past 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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Release v20241124
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