Instructions to use eadx/Rio-3.5-Open-397B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eadx/Rio-3.5-Open-397B with Transformers:

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

pipe = pipeline("image-text-to-text", model="eadx/Rio-3.5-Open-397B")
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("eadx/Rio-3.5-Open-397B")
model = AutoModelForMultimodalLM.from_pretrained("eadx/Rio-3.5-Open-397B")
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 eadx/Rio-3.5-Open-397B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eadx/Rio-3.5-Open-397B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eadx/Rio-3.5-Open-397B",
		"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/eadx/Rio-3.5-Open-397B

SGLang

How to use eadx/Rio-3.5-Open-397B 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 "eadx/Rio-3.5-Open-397B" \
    --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": "eadx/Rio-3.5-Open-397B",
		"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 "eadx/Rio-3.5-Open-397B" \
        --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": "eadx/Rio-3.5-Open-397B",
		"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 eadx/Rio-3.5-Open-397B with Docker Model Runner:
```
docker model run hf.co/eadx/Rio-3.5-Open-397B
```

Rio-3.5-Open-397B / README.md

eadx

Duplicate from prefeitura-rio/Rio-3.5-Open-397B

ef2437a 25 days ago

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	---
	library_name: transformers
	language:
	- pt
	- en
	license: mit
	base_model:
	- Qwen/Qwen3.5-397B-A17B
	pipeline_tag: image-text-to-text
	---

	# Rio 3.5 Open 397B

	![Rio 3.5 Open 397B benchmark results](rio-3.5-open-benchmarks.png)

	Rio 3.5 Open 397B is a frontier-class general-purpose AI model developed by [IplanRIO](https://iplanrio.rio.rj.gov.br/), the municipal IT company of Rio de Janeiro's city government. Post-trained from Qwen 3.5 397B, Rio 3.5 Open 397B delivers state-of-the-art open-model performance across agentic coding, mathematics, STEM, multilingual, and multimodal benchmarks — surpassing its base model by significant margins and competing with the world's best open and proprietary models.

	Rio 3.5 Open 397B features SwiReasoning, a training-free inference framework based on [Shi et al. (2025)](https://arxiv.org/abs/2510.05069) that dynamically switches between explicit chain-of-thought and latent-space reasoning, guided by entropy-based confidence signals. This enables both higher accuracy and dramatically improved token efficiency. This model was explicitly trained to maximize the efficiency gained via latent reasoning.

	## Key Features

	- 397B total / 17B active parameters (Mixture-of-Experts)
	- 1,010,000 token (1M) context window
	- SwiReasoning integration — dynamic explicit/latent reasoning switching for Pareto-superior accuracy and efficiency
	- General-purpose — strong agentic coding, reasoning, instruction-following, and multimodal performance
	- Post-trained from Qwen 3.5 397B
	- Multilingual — strong performance in Portuguese, English, Chinese, and dozens of other languages
	- MIT License — fully open for commercial and research use

	## Benchmark Results

	### Agentic Coding & Software Engineering

	\| Benchmark \| Rio 3.5 Open 397B \| Qwen 3.5 397B (base) \| Qwen 3.7 Plus \| DeepSeek V4 Pro \| Kimi-K2.6 \| GPT 5.5 \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| Terminal-Bench 2.1 \| 70.8 \| 52.5 \| 70.3 \| 67.9 \| 66.7 \| 78.2 \|
	\| DeepSWE \| 23.0 \| 6.0 \| – \| 8.0 \| 24.0 \| 70.0 \|
	\| SWE-Bench Pro \| 58.1 \| 50.9 \| 57.6 \| 59.0 \| 59.5 \| 58.6 \|
	\| SWE-Bench Verified \| 80.2 \| 76.2 \| 77.7 \| 80.6 \| 80.2 \| 82.9 \|
	\| SWE-Bench Multilingual \| 77.0 \| 69.3 \| 75.8 \| 76.2 \| 76.7 \| – \|

	### Knowledge & Reasoning

	\| Benchmark \| Rio 3.5 Open 397B \| Qwen 3.5 397B (base) \| Qwen 3.7 Plus \| DeepSeek V4 Pro \| Kimi-K2.6 \| GPT 5.5 \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| GPQA Diamond \| 90.9 \| 88.4 \| 90.3 \| 90.1 \| 90.5 \| 93.6 \|
	\| HLE \| 36.5 \| 28.7 \| 34.7 \| 37.7 \| 36.4 \| 41.4 \|
	\| MMLU-Pro \| 88.0 \| 87.8 \| 88.5 \| 87.5 \| 87.1 \| – \|
	\| MMLU-Redux \| 94.6 \| 94.9 \| 94.5 \| 94.8 \| 95.3 \| – \|
	\| SuperGPQA \| 72.3 \| 70.4 \| 71.4 \| 69.9 \| 71.3 \| – \|
	\| Apex \| 29.2 \| 9.4 \| 22.7 \| 38.3 \| 24.0 \| 80.2 \|

	### Mathematics

	\| Benchmark \| Rio 3.5 Open 397B \| Qwen 3.5 397B (base) \| Qwen 3.7 Plus \| DeepSeek V4 Pro \| Kimi-K2.6 \| GPT 5.5 \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| HMMT 2026 Feb \| 93.9 \| 87.9 \| 92.9 \| 95.2 \| 92.7 \| 98.5 \|
	\| IMOAnswerBench \| 89.5 \| 80.9 \| 86.0 \| 89.8 \| 86.0 \| – \|

	### Multilingual

	\| Benchmark \| Rio 3.5 Open 397B \| Qwen 3.5 397B (base) \| Qwen 3.7 Plus \| DeepSeek V4 Pro \| Kimi-K2.6 \| GPT 5.5 \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| MMMLU \| 89.8 \| 88.5 \| 89.0 \| 87.9 \| 87.5 \| – \|
	\| MMLU-ProX \| 85.6 \| 84.7 \| 85.4 \| 83.9 \| 83.7 \| – \|

	### Multimodal

	\| Benchmark \| Rio 3.5 Open 397B \| Qwen 3.5 397B (base) \| Qwen 3.7 Plus \| DeepSeek V4 Pro \| Kimi-K2.6 \| GPT 5.5 \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| MMMU-Pro \| 78.4 \| 79.0 \| 79.0 \| – \| 79.4 \| 81.2 \|
	\| MathVision \| 89.1 \| 88.6 \| 90.3 \| – \| 87.4 \| – \|
	\| VideoMMMU \| 81.6 \| 84.7 \| 85.4 \| – \| – \| 86.4 \|

	### Agents & Instruction Following

	\| Benchmark \| Rio 3.5 Open 397B \| Qwen 3.5 397B (base) \| Qwen 3.7 Plus \| DeepSeek V4 Pro \| Kimi-K2.6 \| GPT 5.5 \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| MCP-Atlas \| 74.2 \| 74.2 \| 73.2 \| 73.6 \| 66.6 \| 75.3 \|
	\| IFBench \| 78.4 \| 76.5 \| 79.1 \| 77.0 \| 76.0 \| 76.0 \|
	\| IFEval \| 93.4 \| 92.6 \| 94.6 \| 91.9 \| 94.5 \| – \|

	### Economic Value

	\| Benchmark \| Rio 3.5 Open 397B \| Qwen 3.5 397B (base) \| Qwen 3.7 Plus \| DeepSeek V4 Pro \| Kimi-K2.6 \| GPT 5.5 \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| GDPval (estimated) \| 1533 \| 1200 \| 1520 \| 1554 \| 1482 \| 1769 \|

	### Gains Over Base Model (Qwen 3.5 397B)

	\| Benchmark \| Base Model \| Rio 3.5 Open 397B \| Δ \|
	\|:---\|:---:\|:---:\|:---:\|
	\| Terminal-Bench 2.1 \| 52.5 \| 70.8 \| +18.3 \|
	\| DeepSWE \| 6.0 \| 23.0 \| +17.0 \|
	\| SWE-Bench Pro \| 50.9 \| 58.1 \| +7.2 \|
	\| SWE-Bench Verified \| 76.2 \| 80.2 \| +4.0 \|
	\| SWE-Bench Multilingual \| 69.3 \| 77.0 \| +7.7 \|
	\| GPQA Diamond \| 88.4 \| 90.9 \| +2.5 \|
	\| HLE \| 28.7 \| 36.5 \| +7.8 \|
	\| HMMT 2026 Feb \| 87.9 \| 93.9 \| +6.0 \|
	\| IMOAnswerBench \| 80.9 \| 89.5 \| +8.6 \|
	\| Apex \| 9.4 \| 29.2 \| +19.8 \|
	\| GDPval (estimated) \| 1200 \| 1533 \| +333 \|

	## SwiReasoning: Latent/Explicit Reasoning

	Rio 3.5 Open 397B integrates [SwiReasoning](https://arxiv.org/abs/2510.05069) (Shi et al., 2025), a training-free inference framework that dynamically alternates between two reasoning modes:

	- Explicit reasoning — standard chain-of-thought in natural language, where the model commits tokens to a single reasoning path
	- Latent reasoning — continuous reasoning in hidden space, where the model explores multiple implicit paths simultaneously without emitting tokens

	The switching is governed by block-wise confidence estimated from entropy trends in the next-token distribution. When confidence is low (entropy trending upward), the model enters latent mode to explore alternatives. When confidence recovers, it switches back to explicit mode to commit to a solution.

	This approach achieves a Pareto-superior trade-off: higher accuracy at unlimited budgets and dramatically better token efficiency under constrained budgets. As with previous Rio generations, the model was post-trained to maximize the gains obtained from latent reasoning.

	## How to Use

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "prefeitura-rio/Rio-3.5-Open-397B"

	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype="auto",
	device_map="auto"
	)

	prompt = "Write a poem about Rio de Janeiro."

	messages = [
	{"role": "user", "content": prompt}
	]

	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer([text], return_tensors="pt").to(model.device)

	outputs = model.generate(
	**inputs,
	max_new_tokens=81920,
	temperature=0.6,
	top_p=0.95,
	)

	response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
	print(response)
	```

	### Using with vLLM

	```bash
	vllm serve prefeitura-rio/Rio-3.5-Open-397B \
	--tensor-parallel-size 8 \
	--max-model-len 1048576 \
	--trust-remote-code
	```

	### Using with SGLang

	```bash
	python -m sglang.launch_server \
	--model-path prefeitura-rio/Rio-3.5-Open-397B \
	--tp 8 \
	--context-length 1048576 \
	--trust-remote-code
	```

	## Model Details

	\| \| \|
	\|:---\|:---\|
	\| Developer \| IplanRIO — Empresa Municipal de Informática e Planejamento S.A. \|
	\| Base Model \| Qwen 3.5 397B \|
	\| Architecture \| Mixture-of-Experts (MoE) Transformer \|
	\| Total Parameters \| ~397B \|
	\| Active Parameters \| ~17B \|
	\| Context Length \| 1,010,000 tokens (1M) \|
	\| Training Method \| Post-training \|
	\| Inference Enhancement \| SwiReasoning (latent/explicit switching) \|
	\| License \| MIT \|
	\| Languages \| Multilingual (en, pt, zh, ja, ko, fr, de, es, ar, and more) \|

	## Citation

	If you use SwiReasoning, please also cite:

	```bibtex
	@misc{shi2025swireasoning,
	title={SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs},
	author={Dachuan Shi et al.},
	year={2025},
	eprint={2510.05069},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	## Acknowledgments

	Rio 3.5 Open 397B is built upon the exceptional work of the [Qwen Team](https://github.com/QwenLM) and their Qwen 3.5 model family. We also acknowledge the authors of [SwiReasoning](https://github.com/sdc17/SwiReasoning) for their innovative inference framework.

	Developed in Rio de Janeiro 🇧🇷 by [IplanRIO](https://iplanrio.rio.rj.gov.br/).