Instructions to use vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B")
model = AutoModelForMultimodalLM.from_pretrained("vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B",
		"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/vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B

SGLang

How to use vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B 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 "vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B",
		"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 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 "vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B with Docker Model Runner:
```
docker model run hf.co/vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B
```

Nous Hermes 2 Pro + Xtuner Llava v1.1 - Llama 3 8B

Nous Hermes 2 Pro's LLaMA weights + Xtuner Llava's mm_projector & vision_tower weights.

Good QA + Function Calling + JSON Mode + Vision Multimodal

GGUFs:

Nous Hermes 2 pro: https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF
Xtuner LLaVA v1.1: https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf

Test code:

import requests
from PIL import Image

import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration

model_id = "vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B"

prompt = ("<|im_start|>user\n<image>\nWhat are these?<|im_end|>"
          "<|im_start|>assistant\n")
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"

model = LlavaForConditionalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    low_cpu_mem_usage=True, 
).to(0)

processor = AutoProcessor.from_pretrained(model_id)


raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)

output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))

Example:

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Safetensors

Model size

8B params

Tensor type

F32

F16

Model tree for vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B

Base model

NousResearch/Meta-Llama-3-8B

Finetuned

NousResearch/Hermes-2-Pro-Llama-3-8B

Finetuned

(64)

this model

vonjack
/

Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B

Nous Hermes 2 Pro + Xtuner Llava v1.1 - Llama 3 8B

Model tree for vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B

Datasets used to train vonjack/Nous-Hermes-2-Pro-Xtuner-LLaVA-v1_1-Llama-3-8B