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Poro 34B AWQ by gradjitta

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  4-bit   Autotrain compatible   Awq   Bloom   Endpoints compatible   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/gradjitta/Poro-34B-AWQ 

Poro 34B AWQ Benchmarks

LLME Score: 0.13134
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Poro 34B AWQ (gradjitta/Poro-34B-AWQ)
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Poro 34B AWQ Parameters and Internals

Model Type 
Causal Language Model
Additional Notes 
The model has been quantized by gradjitta with specific quantization configurations catering to efficient model size and performance optimizations.
Input Output 
Input Format:
Accepts plain text prompts.
Accepted Modalities:
text
Output Format:
Generates text based on input prompts.
Performance Tips:
Ensure inputs are text and appropriately formatted for causal language model inferences.
LLM NamePoro 34B AWQ
Repository 🤗https://huggingface.co/gradjitta/Poro-34B-AWQ 
Model Size34b
Required VRAM21 GB
Updated2025-06-18
Maintainergradjitta
Model Typebloom
Model Files  11.8 GB: 1-of-2   9.2 GB: 2-of-2
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureBloomForCausalLM
Licenseapache-2.0
Transformers Version4.35.1
Tokenizer ClassBloomTokenizer
Padding Token<pad>
Vocabulary Size128000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than gradjitta/Poro-34B-AWQ.

Rank the Poro 34B AWQ 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 v20241124
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