Instructions to use Accio-Lab/occamy-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Accio-Lab/occamy-1.0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Accio-Lab/occamy-1.0")
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("Accio-Lab/occamy-1.0")
model = AutoModelForMultimodalLM.from_pretrained("Accio-Lab/occamy-1.0", 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 Accio-Lab/occamy-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Accio-Lab/occamy-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Accio-Lab/occamy-1.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Accio-Lab/occamy-1.0

SGLang

How to use Accio-Lab/occamy-1.0 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 "Accio-Lab/occamy-1.0" \
    --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": "Accio-Lab/occamy-1.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Accio-Lab/occamy-1.0" \
        --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": "Accio-Lab/occamy-1.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Accio-Lab/occamy-1.0 with Docker Model Runner:
```
docker model run hf.co/Accio-Lab/occamy-1.0
```

GGUF Release: Handcrafted APEX-I-MiniPlus-V2 (3.38 BPW) + Q8_0 mmproj Vision Projector

by IsValorum - opened 5 days ago

Discussion

IsValorum

5 days ago

Hugging Face CLI

Hi Accio-Lab team and community!

I have created and released a custom, handcrafted APEX-I-MiniPlus-V2 (3.38 BPW) quantization of Occamy-1.0, bundled with its dedicated high-precision Q8_0 vision projector (mmproj):
👉 IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF

Key Technical Highlights:

100% Handcrafted IQ-Enhanced Architecture: Built with tensor-by-tensor rules using importance matrix calibration (imatrix). Sensitive layers and attention boundaries are upgraded 1:1 to non-linear IQ3_S and IQ4_NL variants without dropping bits, while MoE experts sit at IQ3_XXS (>3 BPW floor).
High-Precision Vision Projector: Includes mmproj-Accio-Lab_occamy-1.0-Q8_0.gguf (614 MB) ensuring near-lossless OCR, visual document parsing, and agentic UI capabilities.
Budget Hardware / 4GB VRAM Capable: Empirically verified in Unsloth Studio. Uses only ~3.8 GB VRAM with remainder in RAM (DDR4 3200), delivering 23 to 26+ tok/s generation and 300 to 410 tok/s prefill.
Full Context Scalability: Fully verified for 24GB GPUs supporting native 256k context with Q8 KV cache.

Feel free to check it out!

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