Instructions to use deepreinforce-ai/Ornith-1.0-397B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepreinforce-ai/Ornith-1.0-397B with Transformers:

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

pipe = pipeline("text-generation", model="deepreinforce-ai/Ornith-1.0-397B")
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("deepreinforce-ai/Ornith-1.0-397B")
model = AutoModelForMultimodalLM.from_pretrained("deepreinforce-ai/Ornith-1.0-397B")
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 deepreinforce-ai/Ornith-1.0-397B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepreinforce-ai/Ornith-1.0-397B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepreinforce-ai/Ornith-1.0-397B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepreinforce-ai/Ornith-1.0-397B

SGLang

How to use deepreinforce-ai/Ornith-1.0-397B 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 "deepreinforce-ai/Ornith-1.0-397B" \
    --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": "deepreinforce-ai/Ornith-1.0-397B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "deepreinforce-ai/Ornith-1.0-397B" \
        --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": "deepreinforce-ai/Ornith-1.0-397B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepreinforce-ai/Ornith-1.0-397B with Docker Model Runner:
```
docker model run hf.co/deepreinforce-ai/Ornith-1.0-397B
```

Can not process input image ！

#16

by beginor - opened 11 days ago

Discussion

beginor

11 days ago

When upload an image， output is always !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

beginor

11 days ago

screen shot with opencode

krustik

11 days ago

I think thats problem of Opencoder itself. I also have really failed results with their new Desktop app, it just cant work with local models at all (their cli i haven't tested yet), it waste tokens for useless reports about its work. And horribly slow, in fact i discovered that connecting it direct with llama.cpp is much slower than connecting with Textgen+llama.cpp-where faster, dont know why...
The only thing i like is ability to add project with all its files, its the noticible problem how models dont see files and predict blindly, even if i'll give full link to repository to chatGPT or GLM in online Z service - they can't read all files code(and you limited by symbols number to write any code).
Anyone knows other similar open apps?

beginor

11 days ago

•

edited 11 days ago

I have tried with claude code and codex, which can not process vision input too !

AyRick

7 days ago

It’s not Opencode, it’s the same with every client

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment