Instructions to use ornith-ai/Ornith-1.0-35B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ornith-ai/Ornith-1.0-35B-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="ornith-ai/Ornith-1.0-35B-FP8")
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("ornith-ai/Ornith-1.0-35B-FP8")
model = AutoModelForMultimodalLM.from_pretrained("ornith-ai/Ornith-1.0-35B-FP8", 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 ornith-ai/Ornith-1.0-35B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ornith-ai/Ornith-1.0-35B-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ornith-ai/Ornith-1.0-35B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ornith-ai/Ornith-1.0-35B-FP8

SGLang

How to use ornith-ai/Ornith-1.0-35B-FP8 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 "ornith-ai/Ornith-1.0-35B-FP8" \
    --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": "ornith-ai/Ornith-1.0-35B-FP8",
		"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 "ornith-ai/Ornith-1.0-35B-FP8" \
        --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": "ornith-ai/Ornith-1.0-35B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ornith-ai/Ornith-1.0-35B-FP8 with Docker Model Runner:
```
docker model run hf.co/ornith-ai/Ornith-1.0-35B-FP8
```

Ornith-1.0-35B-FP8 Vision Path Produces Degenerate Output

by kickxxmatt - opened Jul 2

Discussion

kickxxmatt

Jul 2

Does anyone facing same issue?

Model: deepreinforce-ai/Ornith-1.0-35B-FP8
Inference Backend: vLLM v0.23.1rc1 (custom NVIDIA build)
Hardware: NVIDIA GB10 (DGX Spark, 128GB unified memory)

Issue:
When sending image input to the FP8 variant, the model produces degenerate output consisting entirely of ! characters, regardless of whether thinking mode is enabled or disabled.
Reproduction:
bashBASE64=$(base64 -w 0 image.jpg)
curl -X POST http://localhost:8000/v1/chat/completions
-H "Content-Type: application/json"
-d "{
"model": "ornith-35b",
"messages": [{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,${BASE64}"}},
{"type": "text", "text": "describe this image"}
]}],
"max_tokens": 200,
"chat_template_kwargs": {"enable_thinking": false}
}"
Output:
json{
"choices": [{
"message": {
"content": "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!",
"reasoning": null
},
"finish_reason": "length"
}],
"usage": {"prompt_tokens": 561, "completion_tokens": 200}
}

kickxxmatt

Jul 2

docker run -d --restart unless-stopped --name vllm-ornith
--gpus all -p 8000:8000
--ipc=host
-e HF_TOKEN="$HF_TOKEN"
-v ~/.cache/huggingface:/root/.cache/huggingface
vllm-node
vllm serve deepreinforce-ai/Ornith-1.0-35B-FP8
--served-model-name ornith-35b
--host 0.0.0.0 --port 8000
--max-model-len 131072
--gpu-memory-utilization 0.5
--enable-prefix-caching
--enable-auto-tool-choice --tool-call-parser qwen3_xml
--reasoning-parser qwen3
--trust-remote-code

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