Instructions to use OS-Software/Ornith-1.0-9B-heretic-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OS-Software/Ornith-1.0-9B-heretic-ja with Transformers:

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

pipe = pipeline("text-generation", model="OS-Software/Ornith-1.0-9B-heretic-ja")
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("OS-Software/Ornith-1.0-9B-heretic-ja")
model = AutoModelForMultimodalLM.from_pretrained("OS-Software/Ornith-1.0-9B-heretic-ja", 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 OS-Software/Ornith-1.0-9B-heretic-ja with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OS-Software/Ornith-1.0-9B-heretic-ja"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OS-Software/Ornith-1.0-9B-heretic-ja",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OS-Software/Ornith-1.0-9B-heretic-ja

SGLang

How to use OS-Software/Ornith-1.0-9B-heretic-ja 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 "OS-Software/Ornith-1.0-9B-heretic-ja" \
    --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": "OS-Software/Ornith-1.0-9B-heretic-ja",
		"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 "OS-Software/Ornith-1.0-9B-heretic-ja" \
        --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": "OS-Software/Ornith-1.0-9B-heretic-ja",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OS-Software/Ornith-1.0-9B-heretic-ja with Docker Model Runner:
```
docker model run hf.co/OS-Software/Ornith-1.0-9B-heretic-ja
```

OS-Software commited on Jun 27

Commit

1691479

verified ·

1 Parent(s): 33e8f2c

Update README.md

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README.md +7 -2

README.md CHANGED Viewed

@@ -30,11 +30,16 @@ tags:
 | **KL divergence** | 0.0323 | 0 *(by definition)* |
 | **Refusals** | 0/100 | 93/100 |
 -----
-<img width="600px" src="assets/ornith_logo.png">
 [![Ornith Blog](https://img.shields.io/badge/%F0%9F%A6%A2%EF%B8%8F%20Ornith%20Blog%20-FD8E5B)](https://deep-reinforce.com/ornith.html)
@@ -50,7 +55,7 @@ Highlights:
 - **Self-Improving Training Framework**:  Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model  discovers better search trajectories and generates higher-quality solutions.
 - **Licence**: MIT licensed, globally accessible, and free from regional limitations.
-<img style="width: 100%; max-width: 900px;" src="assets/ornith_9b_eval.png" alt="Ornith 9B Benchmark Results" title="Ornith 9B Benchmark Results">
 ## Ornith 1.0 9B

 | **KL divergence** | 0.0323 | 0 *(by definition)* |
 | **Refusals** | 0/100 | 93/100 |
+Note: Performance testing, including the measurement of refusal rates, was conducted using Japanese datasets ([harmless_alpaca_ja](https://huggingface.co/datasets/OS-Software/harmless_alpaca_ja), [harmful_behaviors_ja](https://huggingface.co/datasets/OS-Software/harmful_behaviors_ja)).
+## GGUF Version
+GGUF quantizations available [here](https://huggingface.co/OS-Software/Ornith-1.0-9B-heretic-ja-GGUF)
 -----
+<img width="600px" src="https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B/resolve/main/assets/ornith_logo.png">
 [![Ornith Blog](https://img.shields.io/badge/%F0%9F%A6%A2%EF%B8%8F%20Ornith%20Blog%20-FD8E5B)](https://deep-reinforce.com/ornith.html)
 - **Self-Improving Training Framework**:  Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model  discovers better search trajectories and generates higher-quality solutions.
 - **Licence**: MIT licensed, globally accessible, and free from regional limitations.
+<img style="width: 100%; max-width: 900px;" src="https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B/resolve/main/assets/ornith_9b_eval.png" alt="Ornith 9B Benchmark Results" title="Ornith 9B Benchmark Results">
 ## Ornith 1.0 9B