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

Libraries

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

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

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

leonardlin commited on 19 days ago

Commit

1991f8f

verified ·

1 Parent(s): e4e8a1d

Update README.md

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link to MTP models

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README.md CHANGED Viewed

@@ -27,6 +27,8 @@ The original model is Ornith-1.0-35B, a Qwen3.5 MoE-family reasoning and
 agentic coding model released by DeepReinforce. This repository only changes
 the checkpoint representation; it does not introduce additional fine-tuning.
 ## Quantization Summary
 - Source model: [`deepreinforce-ai/Ornith-1.0-35B`](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B)

 agentic coding model released by DeepReinforce. This repository only changes
 the checkpoint representation; it does not introduce additional fine-tuning.
+This model is a faithful quant of the original Ornith-1.0-35B and therefore **does not** have an MTP head, however, we have also uploaded a [shisa-ai/Ornith-1.0-35B-FP8-BLOCK-MTP](https://huggingface.co/shisa-ai/Ornith-1.0-35B-FP8-BLOCK-MTP) model that grafts the Qwen 3.6 35B-A3B official MTP head onto the model. This is the best-performing MTP and based on our testing it gives a >20% throughput uplift for coding workloads (tested on RTX PRO 6000 and vLLM v0.23.0).
 ## Quantization Summary
 - Source model: [`deepreinforce-ai/Ornith-1.0-35B`](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B)