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
```

Add official GGUF and NVFP4 download links

by Eang - opened 3 days ago

base: refs/heads/main

←

from: refs/pr/4

Discussion Files changed

+14

-0

Files changed (1) hide show

README.md +14 -0

README.md CHANGED Viewed

@@ -3,6 +3,8 @@ license: apache-2.0
 library_name: transformers
 pipeline_tag: image-text-to-text
 base_model: Qwen/Qwen3.6-35B-A3B
 tags:
 - agent
 - agentic
@@ -30,6 +32,7 @@ tags:
 <div align="center" style="line-height: 1;">
   <a href="https://accio-lab.github.io/occamy/"><img alt="Project Website" src="https://img.shields.io/badge/Website-Occamy--1.0-087F6A"></a>
   <a href="https://huggingface.co/Accio-Lab/Occamy-1.0"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Model-Occamy--1.0-FFD21E"></a>
   <a href="https://github.com/Accio-Lab/Dressage"><img alt="Dressage" src="https://img.shields.io/badge/Training-Dressage-087F6A"></a>
   <a href="https://arxiv.org/pdf/2609.11977"><img alt="Technical Report" src="https://img.shields.io/badge/%F0%9F%93%84%20Report-Occamy--1.0-B31B1B"></a>
   <a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue"></a>
@@ -38,9 +41,20 @@ tags:
 <p align="center">
   <a href="https://accio-lab.github.io/occamy/">Project Website</a> &nbsp;|&nbsp;
   <a href="https://huggingface.co/Accio-Lab/Occamy-1.0">Model Weights</a> &nbsp;|&nbsp;
   <a href="https://github.com/Accio-Lab/Dressage">Training Framework</a>
 </p>
 ## 1. Model Introduction
 Occamy-1.0 is a compact agentic model purpose-built for real-world co-work: long-horizon, stateful tasks that require coordinated use of search, code, tools, files, structured APIs, and productivity software. Starting from the post-trained [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) checkpoint, Occamy concentrates further training on reliable execution, persistent state tracking, recovery, and follow-through rather than relearning general capabilities from scratch.

 library_name: transformers
 pipeline_tag: image-text-to-text
 base_model: Qwen/Qwen3.6-35B-A3B
+datasets:
+- Accio-Lab/occamy-data-1.0
 tags:
 - agent
 - agentic
 <div align="center" style="line-height: 1;">
   <a href="https://accio-lab.github.io/occamy/"><img alt="Project Website" src="https://img.shields.io/badge/Website-Occamy--1.0-087F6A"></a>
   <a href="https://huggingface.co/Accio-Lab/Occamy-1.0"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Model-Occamy--1.0-FFD21E"></a>
+  <a href="https://huggingface.co/datasets/Accio-Lab/occamy-data-1.0"><img alt="Dataset" src="https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-occamy--data--1.0-FFD21E"></a>
   <a href="https://github.com/Accio-Lab/Dressage"><img alt="Dressage" src="https://img.shields.io/badge/Training-Dressage-087F6A"></a>
   <a href="https://arxiv.org/pdf/2609.11977"><img alt="Technical Report" src="https://img.shields.io/badge/%F0%9F%93%84%20Report-Occamy--1.0-B31B1B"></a>
   <a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue"></a>
 <p align="center">
   <a href="https://accio-lab.github.io/occamy/">Project Website</a> &nbsp;|&nbsp;
   <a href="https://huggingface.co/Accio-Lab/Occamy-1.0">Model Weights</a> &nbsp;|&nbsp;
+  <a href="https://huggingface.co/datasets/Accio-Lab/occamy-data-1.0">Dataset</a> &nbsp;|&nbsp;
   <a href="https://github.com/Accio-Lab/Dressage">Training Framework</a>
 </p>
+## Available Checkpoints
+| Format | Repository | Notes |
+|---|---|---|
+| Original BF16 | [Accio-Lab/occamy-1.0](https://huggingface.co/Accio-Lab/occamy-1.0) | Original model weights and configuration. |
+| GGUF | [Accio-Lab/occamy-1.0-GGUF](https://huggingface.co/Accio-Lab/occamy-1.0-GGUF) | Q4_K_M, Q8_0, and the matching F16 vision projector. Normalize text inputs to NFC; see the model card for tokenizer compatibility. |
+| NVFP4 | [Accio-Lab/occamy-1.0-NVFP4](https://huggingface.co/Accio-Lab/occamy-1.0-NVFP4) | Mixed-precision ModelOpt checkpoint with NVFP4 routed experts. See its model card for the tested runtime and hardware requirements. |
+The quantized releases include bounded regression checks, not a re-run of the full benchmarks below. They do not include MTP weights. FP8 and MTP variants are not listed until released and validated.
 ## 1. Model Introduction
 Occamy-1.0 is a compact agentic model purpose-built for real-world co-work: long-horizon, stateful tasks that require coordinated use of search, code, tools, files, structured APIs, and productivity software. Starting from the post-trained [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) checkpoint, Occamy concentrates further training on reliable execution, persistent state tracking, recovery, and follow-through rather than relearning general capabilities from scratch.