Instructions to use city96/llava-llama-3-8b-v1_1-imat-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use city96/llava-llama-3-8b-v1_1-imat-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="city96/llava-llama-3-8b-v1_1-imat-gguf",
	filename="llava-llama-3-8B-v1_1-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use city96/llava-llama-3-8b-v1_1-imat-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M

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 city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M

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 city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M

Use Docker

docker model run hf.co/city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use city96/llava-llama-3-8b-v1_1-imat-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "city96/llava-llama-3-8b-v1_1-imat-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "city96/llava-llama-3-8b-v1_1-imat-gguf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M

Ollama
How to use city96/llava-llama-3-8b-v1_1-imat-gguf with Ollama:
```
ollama run hf.co/city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M
```

Unsloth Studio

How to use city96/llava-llama-3-8b-v1_1-imat-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 city96/llava-llama-3-8b-v1_1-imat-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 city96/llava-llama-3-8b-v1_1-imat-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for city96/llava-llama-3-8b-v1_1-imat-gguf to start chatting

Docker Model Runner
How to use city96/llava-llama-3-8b-v1_1-imat-gguf with Docker Model Runner:
```
docker model run hf.co/city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M
```

Lemonade

How to use city96/llava-llama-3-8b-v1_1-imat-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull city96/llava-llama-3-8b-v1_1-imat-gguf:Q4_K_M

Run and chat with the model

lemonade run user.llava-llama-3-8b-v1_1-imat-gguf-Q4_K_M

List all available models

lemonade list

This is an imatrix gguf conversion of xtuner/llava-llama-3-8b-v1_1-transformers.

Mainly intended to be used as the text encoder for Hunyuan Video, but possible to use for vision tasks with the mmproj file from the xtuner gguf repository.

The imatrix dataset used was calibration_datav3.txt by Bartowski, which was used for all quants under Q6_K. Tested against wikitext / no imatrix and it outperformed both.

Note that the vocab_size is different between the transformers (128 320) and the hf (128 256) repositories. This used the former as it was what was used in the official Hunyuan Video code.

IQ quants will be slow in ComfyUI due to using numpy fallback.

Downloads last month: 1,127

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

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8-bit

16-bit

Model tree for city96/llava-llama-3-8b-v1_1-imat-gguf

Base model

xtuner/llava-llama-3-8b-v1_1-transformers

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