Instructions to use xtuner/llava-llama-3-8b-v1_1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xtuner/llava-llama-3-8b-v1_1-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="xtuner/llava-llama-3-8b-v1_1-gguf",
	filename="llava-llama-3-8b-v1_1-f16.gguf",
)

llm.create_chat_completion(
	messages = "\"cats.jpg\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use xtuner/llava-llama-3-8b-v1_1-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf xtuner/llava-llama-3-8b-v1_1-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf xtuner/llava-llama-3-8b-v1_1-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf xtuner/llava-llama-3-8b-v1_1-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf xtuner/llava-llama-3-8b-v1_1-gguf:F16

Use pre-built binary

# 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-llama-3-8b-v1_1-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf xtuner/llava-llama-3-8b-v1_1-gguf:F16

Build from source code

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-llama-3-8b-v1_1-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf xtuner/llava-llama-3-8b-v1_1-gguf:F16

Use Docker

docker model run hf.co/xtuner/llava-llama-3-8b-v1_1-gguf:F16

LM Studio
Jan
Ollama
How to use xtuner/llava-llama-3-8b-v1_1-gguf with Ollama:
```
ollama run hf.co/xtuner/llava-llama-3-8b-v1_1-gguf:F16
```

Unsloth Studio

How to use xtuner/llava-llama-3-8b-v1_1-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

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-llama-3-8b-v1_1-gguf to start chatting

Install Unsloth Studio (Windows)

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-llama-3-8b-v1_1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for xtuner/llava-llama-3-8b-v1_1-gguf to start chatting

Docker Model Runner
How to use xtuner/llava-llama-3-8b-v1_1-gguf with Docker Model Runner:
```
docker model run hf.co/xtuner/llava-llama-3-8b-v1_1-gguf:F16
```

Lemonade

How to use xtuner/llava-llama-3-8b-v1_1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull xtuner/llava-llama-3-8b-v1_1-gguf:F16

Run and chat with the model

lemonade run user.llava-llama-3-8b-v1_1-gguf-F16

List all available models

lemonade list

llava-llama-3-8b-v1_1-gguf / README.md

LZHgrla

Update README.md

402f42a verified about 2 years ago

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datasets:
  - Lin-Chen/ShareGPT4V
pipeline_tag: image-to-text

Model

llava-llama-3-8b-v1_1 is a LLaVA model fine-tuned from meta-llama/Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner.

Note: This model is in GGUF format.

Resources:

GitHub: xtuner
HuggingFace LLaVA format model: xtuner/llava-llama-3-8b-v1_1-transformers
Official LLaVA format model: xtuner/llava-llama-3-8b-v1_1-hf
XTuner LLaVA format model: xtuner/llava-llama-3-8b-v1_1

Details

Model	Visual Encoder	Projector	Resolution	Pretraining Strategy	Fine-tuning Strategy	Pretrain Dataset	Fine-tune Dataset
LLaVA-v1.5-7B	CLIP-L	MLP	336	Frozen LLM, Frozen ViT	Full LLM, Frozen ViT	LLaVA-PT (558K)	LLaVA-Mix (665K)
LLaVA-Llama-3-8B	CLIP-L	MLP	336	Frozen LLM, Frozen ViT	Full LLM, LoRA ViT	LLaVA-PT (558K)	LLaVA-Mix (665K)
LLaVA-Llama-3-8B-v1.1	CLIP-L	MLP	336	Frozen LLM, Frozen ViT	Full LLM, LoRA ViT	ShareGPT4V-PT (1246K)	InternVL-SFT (1268K)

Results

Model	MMBench Test (EN)	MMBench Test (CN)	CCBench Dev	MMMU Val	SEED-IMG	AI2D Test	ScienceQA Test	HallusionBench aAcc	POPE	GQA	TextVQA	MME	MMStar
LLaVA-v1.5-7B	66.5	59.0	27.5	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	61.6	30.4	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	66.4	31.6	36.8	70.1	70.0	72.9	47.7	86.4	62.6	59.0	1469/349	45.1

Quickstart

Download models

# mmproj
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/mmproj-model-f16.gguf

# fp16 llm
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/ggml-model-f16.gguf

# int4 llm
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/ggml-model-int4.gguf

Build environment

Build llama.cpp (docs) .
Build ./llava-cli (docs).

Chat by `./llava-cli`

# fp16
./llava-cli -m ./ggml-model-f16.gguf --mmproj ./mmproj-model-f16.gguf --image YOUR_IMAGE.jpg -c 4096

# int4
./llava-cli -m ./ggml-model-int4.gguf --mmproj ./mmproj-model-f16.gguf --image YOUR_IMAGE.jpg -c 4096

Reproduce

Please refer to docs.

Citation

@misc{2023xtuner,
    title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
    author={XTuner Contributors},
    howpublished = {\url{https://github.com/InternLM/xtuner}},
    year={2023}
}