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TinyLlama 1.1B Step 50K 105B ONNX by mgoin

 »  All LLMs  »  mgoin  »  TinyLlama 1.1B Step 50K 105B ONNX   URL Share it on

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  Autotrain compatible   Deepsparse   Endpoints compatible   Llama   Onnx   Region:us
Model Card on HF 🤗: https://huggingface.co/mgoin/TinyLlama-1.1B-step-50K-105b-ONNX 

TinyLlama 1.1B Step 50K 105B ONNX Benchmarks

LLME Score: 0.12944
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
TinyLlama 1.1B Step 50K 105B ONNX (mgoin/TinyLlama-1.1B-step-50K-105b-ONNX)

TinyLlama 1.1B Step 50K 105B ONNX Parameters and Internals

Model Type 
Sparse, Neural Network Optimizer
Use Cases 
Areas:
Efficiency Optimization, Model Execution Speed
Applications:
Inference acceleration, Deep learning deployments
Primary Use Cases:
Efficient execution of neural networks on CPUs, Sparsity-aware optimizations
Additional Notes 
DeepSparse offers substantial cost savings by enabling high-performance AI execution on commodity hardware without GPUs.
Input Output 
Input Format:
Sparse model format
Accepted Modalities:
text, audio, image
Output Format:
Optimized model execution results
Performance Tips:
Use quantized models and leverage batch processing for better performance.
Release Notes 
Version:
1.0
Date:
2023-02-15
Notes:
Initial release with support for CPU sparsity-based optimizations.
LLM NameTinyLlama 1.1B Step 50K 105B ONNX
Repository 🤗https://huggingface.co/mgoin/TinyLlama-1.1B-step-50K-105b-ONNX 
Model Size105b
Updated2025-02-22
Maintainermgoin
Model Typellama
Model ArchitectureLlamaForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.31.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than mgoin/TinyLlama-1.1B-step-50K-105b-ONNX.

Rank the TinyLlama 1.1B Step 50K 105B ONNX Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
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Text Generation  
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