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

Libraries

How to use deepreinforce-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="deepreinforce-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("deepreinforce-ai/Ornith-1.0-35B-FP8")
model = AutoModelForMultimodalLM.from_pretrained("deepreinforce-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?"}
        ]
    },
]
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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

IDEA: Bitnet 1.58 (a4.8) version in future variants would be so incredible!

by apiarium - opened 11 days ago

Discussion

apiarium

11 days ago

Ornith-1.0-35B is incredible — thanks for releasing the weights. Would the team consider releasing a BitNet 1.58 (a4.8) variant in a future update? For anyone unfamiliar: ternary weights {-1, 0, +1} at 1.58 bits/param plus 4-bit activations means matmul becomes pure integer addition, no floating-point multipliers needed. Microsoft's bitnet.cpp already runs a 100B BitNet model on a single CPU at reading speed (5–7 tok/s) with 2–6× speedups on consumer hardware. The BitNet 1.58 paper (arXiv:2402.17764) showed perplexity parity with FP16 at 7B scale, and the a4.8 paper (arXiv:2411.04965) pushed activations down further with minimal quality loss. Training recipe is public at microsoft/unilm. A BitNet-native Ornith would run offline on ordinary laptops and phones — no GPU needed — which is exactly the kind of accessibility a model this good deserves. Just putting it out there!

apiarium changed discussion status to closed 11 days ago

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