Instructions to use NajeebAhmed/llava_quantized_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NajeebAhmed/llava_quantized_model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NajeebAhmed/llava_quantized_model")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("NajeebAhmed/llava_quantized_model")
model = AutoModelForCausalLM.from_pretrained("NajeebAhmed/llava_quantized_model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NajeebAhmed/llava_quantized_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NajeebAhmed/llava_quantized_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NajeebAhmed/llava_quantized_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NajeebAhmed/llava_quantized_model

SGLang

How to use NajeebAhmed/llava_quantized_model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NajeebAhmed/llava_quantized_model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NajeebAhmed/llava_quantized_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NajeebAhmed/llava_quantized_model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NajeebAhmed/llava_quantized_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NajeebAhmed/llava_quantized_model with Docker Model Runner:
```
docker model run hf.co/NajeebAhmed/llava_quantized_model
```

llava_quantized_model

Commit History

Nov 19, 2023

07:16
Adding `safetensors` variant of this model⁠

SFconvertbot

bb9aa49

Oct 30, 2023

14:33
renaming

NajeebAhmed

d3a85a5
14:32
Upload config.json⁠

NajeebAhmed

8ad280a
09:04
Add quantized model and tokenizer

NajeebAhmed

32ee3ce

Oct 29, 2023

18:24
initial commit

NajeebAhmed

1c7c91f

Commit History

Nov 19, 2023

Adding `safetensors` variant of this model⁠

Oct 30, 2023

renaming

Upload config.json⁠

Add quantized model and tokenizer

Oct 29, 2023

initial commit