Instructions to use DuoNeural/Flicker-E2B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/Flicker-E2B-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="DuoNeural/Flicker-E2B-v1")
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("DuoNeural/Flicker-E2B-v1")
model = AutoModelForMultimodalLM.from_pretrained("DuoNeural/Flicker-E2B-v1", 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 DuoNeural/Flicker-E2B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/Flicker-E2B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DuoNeural/Flicker-E2B-v1",
		"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/DuoNeural/Flicker-E2B-v1

SGLang

How to use DuoNeural/Flicker-E2B-v1 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 "DuoNeural/Flicker-E2B-v1" \
    --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": "DuoNeural/Flicker-E2B-v1",
		"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 "DuoNeural/Flicker-E2B-v1" \
        --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": "DuoNeural/Flicker-E2B-v1",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use DuoNeural/Flicker-E2B-v1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DuoNeural/Flicker-E2B-v1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DuoNeural/Flicker-E2B-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DuoNeural/Flicker-E2B-v1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DuoNeural/Flicker-E2B-v1",
    max_seq_length=2048,
)

Docker Model Runner
How to use DuoNeural/Flicker-E2B-v1 with Docker Model Runner:
```
docker model run hf.co/DuoNeural/Flicker-E2B-v1
```

Flicker-E2B-v1 / README.md

DuoNeural

docs: add DuoNeural research publications section

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	---
	base_model: unsloth/gemma-4-e2b-it
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- gemma4
	license: apache-2.0
	language:
	- en
	---

	# Uploaded finetuned model

	- Developed by: DuoNeural
	- License: apache-2.0
	- Finetuned from model : unsloth/gemma-4-e2b-it

	This gemma4 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

	---

	## DuoNeural

	DuoNeural is an open AI research lab — human + AI in collaboration.

	\| \| \|
	\|---\|---\|
	\| 🤗 HuggingFace \| [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) \|
	\| 🐙 GitHub \| [github.com/DuoNeural](https://github.com/DuoNeural) \|
	\| 🐦 X / Twitter \| [@DuoNeural](https://x.com/DuoNeural) \|
	\| 📧 Email \| duoneural@proton.me \|
	\| 📬 Newsletter \| [duoneural.beehiiv.com](https://duoneural.beehiiv.com) \|
	\| ☕ Support \| [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) \|
	\| 🌐 Site \| [duoneural.com](https://duoneural.com) \|

	### Research Team
	- Jesse — Vision, hardware, direction
	- Archon — AI lab partner, post-training, abliteration, experiments
	- Aura — Research AI, literature synthesis, novel proposals

	Raw updates from the lab: model drops, training results, findings. Subscribe at [duoneural.beehiiv.com](https://duoneural.beehiiv.com).

	### DuoNeural Research Publications

	\| Title \| DOI \|
	\|-------\|-----\|
	\| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) \| [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) \|
	\| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) \| [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) \|
	\| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) \| [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) \|

	Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

	---
	base_model: unsloth/gemma-4-e2b-it
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- gemma4
	license: apache-2.0
	language:
	- en
	---

	# Uploaded finetuned model

	- Developed by: DuoNeural
	- License: apache-2.0
	- Finetuned from model : unsloth/gemma-4-e2b-it

	This gemma4 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

	---

	## DuoNeural

	DuoNeural is an open AI research lab — human + AI in collaboration.

	\| \| \|
	\|---\|---\|
	\| 🤗 HuggingFace \| [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) \|
	\| 🐙 GitHub \| [github.com/DuoNeural](https://github.com/DuoNeural) \|
	\| 🐦 X / Twitter \| [@DuoNeural](https://x.com/DuoNeural) \|
	\| 📧 Email \| duoneural@proton.me \|
	\| 📬 Newsletter \| [duoneural.beehiiv.com](https://duoneural.beehiiv.com) \|
	\| ☕ Support \| [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) \|
	\| 🌐 Site \| [duoneural.com](https://duoneural.com) \|

	### Research Team
	- Jesse — Vision, hardware, direction
	- Archon — AI lab partner, post-training, abliteration, experiments
	- Aura — Research AI, literature synthesis, novel proposals

	Raw updates from the lab: model drops, training results, findings. Subscribe at [duoneural.beehiiv.com](https://duoneural.beehiiv.com).

	### DuoNeural Research Publications

	\| Title \| DOI \|
	\|-------\|-----\|
	\| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) \| [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) \|
	\| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) \| [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) \|
	\| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) \| [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) \|

	Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.