Instructions to use Zero-Vision/Llama-3-MixSenseV1_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zero-Vision/Llama-3-MixSenseV1_1 with Transformers:

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

pipe = pipeline("text-generation", model="Zero-Vision/Llama-3-MixSenseV1_1", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Zero-Vision/Llama-3-MixSenseV1_1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Zero-Vision/Llama-3-MixSenseV1_1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zero-Vision/Llama-3-MixSenseV1_1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zero-Vision/Llama-3-MixSenseV1_1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Zero-Vision/Llama-3-MixSenseV1_1

SGLang

How to use Zero-Vision/Llama-3-MixSenseV1_1 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 "Zero-Vision/Llama-3-MixSenseV1_1" \
    --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": "Zero-Vision/Llama-3-MixSenseV1_1",
		"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 "Zero-Vision/Llama-3-MixSenseV1_1" \
        --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": "Zero-Vision/Llama-3-MixSenseV1_1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Zero-Vision/Llama-3-MixSenseV1_1 with Docker Model Runner:
```
docker model run hf.co/Zero-Vision/Llama-3-MixSenseV1_1
```

Llama-3-MixSenseV1_1 / demo.py

xrx

Initial commit

c3314a5 about 2 years ago

Raw History Blame Contribute Delete

1.28 kB

	import torch
	import transformers
	from transformers import AutoModelForCausalLM, AutoTokenizer
	from PIL import Image
	import warnings
	import os

	os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
	# disable some warnings
	transformers.logging.set_verbosity_error()
	transformers.logging.disable_progress_bar()
	warnings.filterwarnings("ignore")

	# set device
	device = "cuda" # or cpu

	# create model
	model = AutoModelForCausalLM.from_pretrained(
	"Zero-Vision/Llama-3-MixSenseV1_1",
	torch_dtype=torch.float16,
	device_map="auto",
	trust_remote_code=True,
	)
	tokenizer = AutoTokenizer.from_pretrained(
	"Zero-Vision/Llama-3-MixSenseV1_1",
	trust_remote_code=True,
	)
	qs = "describe the image detailly."
	input_ids = model.text_process(qs, tokenizer).to(device)

	image = Image.open("example.jpg")
	image_tensor = model.image_process([image]).to(dtype=model.dtype, device=device)

	# generate
	with torch.inference_mode():
	output_ids = model.generate(
	input_ids,
	images=image_tensor,
	max_new_tokens=2048,
	use_cache=True,
	eos_token_id=[
	tokenizer.eos_token_id,
	tokenizer.convert_tokens_to_ids(["<\|eot_id\|>"])[0],
	],
	)
	print(tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip())