Cognitivecomputations Dolphin 2.8 Gemma 2B HQQ 1bit Smashed by PrunaAI

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  1bit   Autotrain compatible Base model:cognitivecomputatio... Base model:finetune:cognitivec...   Conversational   Endpoints compatible   Gemma   Pruna-ai   Quantized   Region:us

Cognitivecomputations Dolphin 2.8 Gemma 2B HQQ 1bit Smashed Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Cognitivecomputations Dolphin 2.8 Gemma 2B HQQ 1bit Smashed (PrunaAI/cognitivecomputations-dolphin-2.8-gemma-2b-HQQ-1bit-smashed)

Cognitivecomputations Dolphin 2.8 Gemma 2B HQQ 1bit Smashed Parameters and Internals

Model Type 
compressed
Use Cases 
Areas:
model compression, efficiency improvement
Primary Use Cases:
reducing AI model size, improving model inference speed, decreasing energy consumption
Additional Notes 
Model efficiency and metrics provided for first run and sync/async distinction. Compressed using 1bit quantization.
Training Details 
Data Sources:
WikiText
Methodology:
Model is compressed with hqq.
Input Output 
Input Format:
text
Accepted Modalities:
text
Output Format:
text
Performance Tips:
Run efficiency tests under intended use-case conditions.
LLM NameCognitivecomputations Dolphin 2.8 Gemma 2B HQQ 1bit Smashed
Repository ๐Ÿค—https://huggingface.co/PrunaAI/cognitivecomputations-dolphin-2.8-gemma-2b-HQQ-1bit-smashed 
Base Model(s)  cognitivecomputations/dolphin-2.8-gemma-2b   cognitivecomputations/dolphin-2.8-gemma-2b
Model Size2b
Required VRAM1.3 GB
Updated2024-12-22
MaintainerPrunaAI
Model Typegemma
Model Files  1.3 GB
Quantization Type1bit
Model ArchitectureGemmaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.40.0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than PrunaAI/cognitivecomputations-dolphin-2.8-gemma-2b-HQQ-1bit-smashed.

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217