Instructions to use evalengine/unbound-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use evalengine/unbound-e2b with Transformers:

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

pipe = pipeline("image-text-to-text", model="evalengine/unbound-e2b")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("evalengine/unbound-e2b")
model = AutoModelForImageTextToText.from_pretrained("evalengine/unbound-e2b")
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

vLLM

How to use evalengine/unbound-e2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "evalengine/unbound-e2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "evalengine/unbound-e2b",
		"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/evalengine/unbound-e2b

SGLang

How to use evalengine/unbound-e2b 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 "evalengine/unbound-e2b" \
    --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": "evalengine/unbound-e2b",
		"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 "evalengine/unbound-e2b" \
        --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": "evalengine/unbound-e2b",
		"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 evalengine/unbound-e2b with Docker Model Runner:
```
docker model run hf.co/evalengine/unbound-e2b
```

johnsonchromia commited on 4 days ago

Commit

ecb12de

verified ·

1 Parent(s): 1fbe3a5

README: point GGUF section at split-file repo + wllama

Browse files

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README.md +8 -5

README.md CHANGED Viewed

@@ -24,8 +24,8 @@ library_name: transformers
 Uncensored variant of `google/gemma-4-E2B-it` from the [**Chromia**](https://x.com/Chromia) & [**Eval Engine**](https://x.com/eval_engine)
 team. This repo holds the merged HF weights; for the **on-device GGUF builds**
-(Ollama / llama.cpp / LM Studio), see
-[`evalengine/unbound-e2b-gguf`](https://huggingface.co/evalengine/unbound-e2b-gguf).
 ## What this is for
@@ -76,11 +76,14 @@ re-phrasing usually gets through.
 ## Run on-device (GGUF)
-The phone-deployable Q4_K_M build lives in a separate repo:
 ```bash
-ollama pull hf.co/evalengine/unbound-e2b-gguf
-ollama run  hf.co/evalengine/unbound-e2b-gguf
 ```
 ## Run in transformers

 Uncensored variant of `google/gemma-4-E2B-it` from the [**Chromia**](https://x.com/Chromia) & [**Eval Engine**](https://x.com/eval_engine)
 team. This repo holds the merged HF weights; for the **on-device GGUF builds**
+(Ollama / llama.cpp / LM Studio / [wllama](https://github.com/ngxson/wllama) in-browser), see
+[`evalengine/unbound-e2b-GGUF`](https://huggingface.co/evalengine/unbound-e2b-GGUF).
 ## What this is for
 ## Run on-device (GGUF)
+The phone-deployable build lives in
+[`evalengine/unbound-e2b-GGUF`](https://huggingface.co/evalengine/unbound-e2b-GGUF) —
+Q4_K_M / Q6_K / Q8_0, all shipped as split multi-part files (browser-safe via
+wllama; Ollama and llama.cpp auto-stitch on the first part):
 ```bash
+ollama pull hf.co/evalengine/unbound-e2b-GGUF
+ollama run  hf.co/evalengine/unbound-e2b-GGUF
 ```
 ## Run in transformers