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Poro 34B AWQ by TheBloke

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  4-bit   Autotrain compatible   Awq   Base model:lumiopen/poro-34b Base model:quantized:lumiopen/...   Bloom   Dataset:allenai/dolma   Dataset:bigcode/starcoderdata Dataset:cerebras/slimpajama-62...   Dataset:mc4   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TheBloke/Poro-34B-AWQ 

Poro 34B AWQ Benchmarks

LLME Score: 0.13408
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 (TheBloke/Poro-34B-AWQ)
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Poro 34B AWQ Parameters and Internals

Model Type 
bloom
Use Cases 
Areas:
Research, AI development
Applications:
Language understanding, Natural language processing
Primary Use Cases:
Basic translation between English and Finnish
Limitations:
Partially trained model with caution advised in outputs
Considerations:
Intended for research and testing purposes.
Additional Notes 
Model is partially trained; caution is advised in interpreting outputs.
Supported Languages 
languages_supported (Finnish, English, Code), proficiency_level (N/A)
Training Details 
Data Sources:
cerebras/SlimPajama-627B, bigcode/starcoderdata, mc4, allenai/dolma
Data Volume:
1 trillion tokens
Methodology:
Uses ALiBi embeddings to support context length extrapolation at inference time.
Context Length:
2048
Training Time:
N/A
Hardware Used:
LUMI supercomputer with 512 AMD MI250X GPUs
Model Architecture:
Generative pretrained transformer using a BLOOM architecture
Responsible Ai Considerations 
Fairness:
The model is a product of the data it has been trained on, which may contain biases.
Transparency:
No transparency measures specified.
Accountability:
Responsibility lies with developers and users to ensure ethical use.
Mitigation Strategies:
N/A
Input Output 
Input Format:
{prompt}
Accepted Modalities:
text
Output Format:
N/A
Performance Tips:
Use appropriate quantization and model loading techniques for efficient inference.
LLM NamePoro 34B AWQ
Repository 🤗https://huggingface.co/TheBloke/Poro-34B-AWQ 
Model NamePoro 34B
Model CreatorLumiOpen
Base Model(s)  Poro 34B   LumiOpen/Poro-34B
Model Size34b
Required VRAM21 GB
Updated2025-06-17
MaintainerTheBloke
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 Version4.35.2
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 TheBloke/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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Release v20241124
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