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MaziyarPanahi Phi 3 Mini 4K Instruct V0.3 HQQ 1bit Smashed by PrunaAI

By PrunaAI · 5 downloads

MaziyarPanahi Phi 3 Mini 4K Instruct V0.3 HQQ 1bit Smashed is an open-source language model by PrunaAI. Features: 4b LLM, VRAM: 0.9GB, Context: 4K, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

  1bit Base model:finetune:maziyarpan... Base model:maziyarpanahi/calme...   Custom code   Instruct   Phi3   Pruna-ai   Quantized   Region:us

MaziyarPanahi Phi 3 Mini 4K Instruct V0.3 HQQ 1bit Smashed Parameters and Internals

Model Type 
compressed
Use Cases 
Areas:
AI Model Compression
Applications:
Reducing resource usage in AI models
Primary Use Cases:
Improving AI model efficiency
Considerations:
Efficiency results may vary in other settings. It's recommended to test the model in specific use-case conditions.
Additional Notes 
This model uses safetensors format. First run metrics may vary due to CUDA overheads.
Training Details 
Methodology:
Compressed with hqq using WikiText as calibration data.
Input Output 
Performance Tips:
It's recommended to run efficiency tests directly in use-case scenarios to determine if the smashed model benefits your application.
LLM NameMaziyarPanahi Phi 3 Mini 4K Instruct V0.3 HQQ 1bit Smashed
Repository πŸ€—https://huggingface.co/PrunaAI/MaziyarPanahi-Phi-3-mini-4k-instruct-v0.3-HQQ-1bit-smashed 
Base Model(s)  MaziyarPanahi/Phi-3-mini-4k-instruct-v0.3   MaziyarPanahi/Phi-3-mini-4k-instruct-v0.3
Model Size4b
Required VRAM0.9 GB
Updated2025-09-13
MaintainerPrunaAI
Model Typephi3
Instruction-BasedYes
Model Files  0.9 GB
Quantization Type1bit
Model ArchitecturePhi3ForCausalLM
Context Length4096
Model Max Length4096
Transformers Version4.40.0
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than PrunaAI/MaziyarPanahi-Phi-3-mini-4k-instruct-v0.3-HQQ-1bit-smashed.