Lin-Chen/ShareGPT4V
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How to use xtuner/llava-phi-3-mini-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="xtuner/llava-phi-3-mini-gguf", filename="llava-phi-3-mini-f16.gguf", )
llm.create_chat_completion( messages = "\"cats.jpg\"" )
How to use xtuner/llava-phi-3-mini-gguf with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf xtuner/llava-phi-3-mini-gguf:F16 # Run inference directly in the terminal: llama-cli -hf xtuner/llava-phi-3-mini-gguf:F16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf xtuner/llava-phi-3-mini-gguf:F16 # Run inference directly in the terminal: llama-cli -hf xtuner/llava-phi-3-mini-gguf:F16
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf xtuner/llava-phi-3-mini-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf xtuner/llava-phi-3-mini-gguf:F16
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf xtuner/llava-phi-3-mini-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf xtuner/llava-phi-3-mini-gguf:F16
docker model run hf.co/xtuner/llava-phi-3-mini-gguf:F16
How to use xtuner/llava-phi-3-mini-gguf with Ollama:
ollama run hf.co/xtuner/llava-phi-3-mini-gguf:F16
How to use xtuner/llava-phi-3-mini-gguf with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for xtuner/llava-phi-3-mini-gguf to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for xtuner/llava-phi-3-mini-gguf to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for xtuner/llava-phi-3-mini-gguf to start chatting
How to use xtuner/llava-phi-3-mini-gguf with Docker Model Runner:
docker model run hf.co/xtuner/llava-phi-3-mini-gguf:F16
How to use xtuner/llava-phi-3-mini-gguf with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull xtuner/llava-phi-3-mini-gguf:F16
lemonade run user.llava-phi-3-mini-gguf-F16
lemonade list
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf xtuner/llava-phi-3-mini-gguf:F16# Run inference directly in the terminal:
llama-cli -hf xtuner/llava-phi-3-mini-gguf:F16# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf xtuner/llava-phi-3-mini-gguf:F16# Run inference directly in the terminal:
./llama-cli -hf xtuner/llava-phi-3-mini-gguf:F16git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf xtuner/llava-phi-3-mini-gguf:F16# Run inference directly in the terminal:
./build/bin/llama-cli -hf xtuner/llava-phi-3-mini-gguf:F16docker model run hf.co/xtuner/llava-phi-3-mini-gguf:F16llava-phi-3-mini is a LLaVA model fine-tuned from microsoft/Phi-3-mini-4k-instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner.
Note: This model is in GGUF format.
Resources:
| Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset | Pretrain Epoch | Fine-tune Epoch |
|---|---|---|---|---|---|---|---|---|---|
| LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 |
| LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 |
| LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 1 |
| LLaVA-Phi-3-mini | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Full ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 2 |
| Model | MMBench Test (EN) | MMMU Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA | TextVQA | MME | MMStar |
|---|---|---|---|---|---|---|---|---|---|---|---|
| LLaVA-v1.5-7B | 66.5 | 35.3 | 60.5 | 54.8 | 70.4 | 44.9 | 85.9 | 62.0 | 58.2 | 1511/348 | 30.3 |
| LLaVA-Llama-3-8B | 68.9 | 36.8 | 69.8 | 60.9 | 73.3 | 47.3 | 87.2 | 63.5 | 58.0 | 1506/295 | 38.2 |
| LLaVA-Llama-3-8B-v1.1 | 72.3 | 37.1 | 70.1 | 70.0 | 72.9 | 47.7 | 86.4 | 62.6 | 59.0 | 1469/349 | 45.1 |
| LLaVA-Phi-3-mini | 69.2 | 41.4 | 70.0 | 69.3 | 73.7 | 49.8 | 87.3 | 61.5 | 57.8 | 1477/313 | 43.7 |
# mmproj
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/llava-phi-3-mini-mmproj-f16.gguf
# fp16 llm
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/llava-phi-3-mini-f16.gguf
# int4 llm
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/llava-phi-3-mini-int4.gguf
# (optional) ollama fp16 modelfile
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/OLLAMA_MODELFILE_F16
# (optional) ollama int4 modelfile
wget https://huggingface.co/xtuner/llava-phi-3-mini-gguf/resolve/main/OLLAMA_MODELFILE_INT4
ollama
Note: llava-phi-3-mini uses the Phi-3-instruct chat template.
# fp16
ollama create llava-phi3-f16 -f ./OLLAMA_MODELFILE_F16
ollama run llava-phi3-f16 "xx.png Describe this image"
# int4
ollama create llava-phi3-int4 -f ./OLLAMA_MODELFILE_INT4
ollama run llava-phi3-int4 "xx.png Describe this image"
./llava-cli
Note: llava-phi-3-mini uses the Phi-3-instruct chat template.
# fp16
./llava-cli -m ./llava-phi-3-mini-f16.gguf --mmproj ./llava-phi-3-mini-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096
# int4
./llava-cli -m ./llava-phi-3-mini-int4.gguf --mmproj ./llava-phi-3-mini-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096
Please refer to docs.
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}
16-bit
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf xtuner/llava-phi-3-mini-gguf:F16# Run inference directly in the terminal: llama-cli -hf xtuner/llava-phi-3-mini-gguf:F16