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GGUF Quantized LLaVa Phi-2 3B

Original model from marianna13/llava-phi-2-3b.

Provided Files

Name Quant method Bits Size Max RAM required Use case
ggml-model-Q2_K.gguf Q2_K 2 1.17 GB 3.67 GB smallest, significant quality loss - not recommended for most purposes
ggml-model-Q3_K_S.gguf Q3_K_S 3 1.25 GB 3.75 GB very small, high quality loss
ggml-model-Q3_K_M.gguf Q3_K_M 3 1.48 GB 3.98 GB very small, high quality loss
ggml-model-Q4_0.gguf Q4_0 4 1.60 GB 4.10 GB legacy; small, very high quality loss - prefer using Q3_K_M
ggml-model-Q3_K_L.gguf Q3_K_L 3 1.60 GB 4.10 GB small, substantial quality loss
ggml-model-Q4_K_S.gguf Q4_K_S 4 1.62 GB 4.12 GB small, greater quality loss
ggml-model-Q4_K_M.gguf Q4_K_M 4 1.79 GB 4.29 GB medium, balanced quality - recommended
ggml-model-Q5_0.gguf Q5_0 5 1.93 GB 4.43 GB legacy; medium, balanced quality - prefer using Q4_K_M
ggml-model-Q5_K_S.gguf Q5_K_S 5 1.93 GB 4.43 GB large, low quality loss - recommended
ggml-model-Q5_K_M.gguf Q5_K_M 5 2.07 GB 4.57 GB large, very low quality loss - recommended
ggml-model-Q6_K.gguf Q6_K 6 2.29 GB 4.79 GB very large, extremely low quality loss
ggml-model-Q8_0.gguf Q8_0 8 2.96 GB 5.46 GB very large, extremely low quality loss - not recommended

ORIGINAL MODEL CARD

Model Card for LLaVa-Phi-2-3B

Model Details

Model Description

  • Developed by: LAION, SkunkworksAI & Ontocord
  • Model type: LLaVA is an open-source chatbot trained by fine-tuning Phi-2 on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture
  • Finetuned from model: Phi-2
  • License: MIT
  • Demo: llava-phi-2-3b-demo

Model Sources

Evaluation

Benchmarks

Model Parameters SQA GQA TextVQA POPE
LLaVA-1.5 7.3B 68.0 62.0 58.3 85.3
MC-LLaVA-3B 3B - 49.6 38.59 -
LLaVA-Phi 3B 68.4 - 48.6 85.0
moondream1 1.6B - 56.3 39.8 -
llava-phi-2-3b 3B 69.0 51.2 47.0 86.0

Image Captioning (MS COCO)

Model BLEU_1 BLEU_2 BLEU_3 BLEU_4 METEOR ROUGE_L CIDEr SPICE
llava-1.5-7b 75.8 59.8 45 33.3 29.4 57.7 108.8 23.5
llava-phi-2-3b 67.7 50.5 35.7 24.2 27.0 52.4 85.0 20.7
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