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 1 day ago

Commit

0539a6b

verified ·

1 Parent(s): cd98bb6

Add release-suite benchmarks (GPQA-Diamond, BBH); fix Ollama pull command

Browse files

Files changed (1) hide show

README.md +10 -3

README.md CHANGED Viewed

@@ -40,8 +40,15 @@ are at [`evalengine/unbound-e2b-GGUF`](https://huggingface.co/evalengine/unbound
 | TruthfulQA mc2 (`--limit 100`) | 0.458 | 0.465 | +0.7 pt |
 | MMLU (`--limit 100`) | 0.291 | 0.282 | −0.9 pt |
 | GSM8K (`--limit 100`) | 0.125 | 0.120 | −0.5 pt |
 | KL divergence vs base | 0 | 3.76 | (SFT-expected) |
 ## Sampling
 - **Creative / open-ended** → Gemma defaults: `temperature=1.0, top_p=0.95, top_k=64`.
@@ -54,9 +61,9 @@ Some edge-case prompts may deflect on the first ask; a re-ask usually gets throu
 ## Use
 ```bash
-# on-device (GGUF)
-ollama pull hf.co/evalengine/unbound-e2b-GGUF
-ollama run  hf.co/evalengine/unbound-e2b-GGUF
 ```
 ```python

 | TruthfulQA mc2 (`--limit 100`) | 0.458 | 0.465 | +0.7 pt |
 | MMLU (`--limit 100`) | 0.291 | 0.282 | −0.9 pt |
 | GSM8K (`--limit 100`) | 0.125 | 0.120 | −0.5 pt |
+| GPQA-Diamond (`--limit 200`) | 22.73% | 21.21% | −1.5 pt (within stderr) |
+| BBH macro (24 tasks, `--limit 200`) | 41.07% | 39.97% | −1.1 pt |
 | KL divergence vs base | 0 | 3.76 | (SFT-expected) |
+Capability holds within ≤1.5 pp of base on every axis; refusal collapses
+from 98% → 4%. GPQA-Diamond + BBH are the lm-eval-harness "release" suite at
+`--limit 200` — base and finetune through the same harness, so the **delta**
+is apples-to-apples.
 ## Sampling
 - **Creative / open-ended** → Gemma defaults: `temperature=1.0, top_p=0.95, top_k=64`.
 ## Use
 ```bash
+# on-device (Ollama Registry — single-file Q4_K_M, identity-grounded Modelfile)
+ollama pull evalengine/unbound-e2b
+ollama run  evalengine/unbound-e2b
 ```
 ```python