Instructions to use evalengine/unbound-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use evalengine/unbound-e4b with Transformers:

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

pipe = pipeline("image-text-to-text", model="evalengine/unbound-e4b")
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-e4b")
model = AutoModelForImageTextToText.from_pretrained("evalengine/unbound-e4b")
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-e4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "evalengine/unbound-e4b"
# 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-e4b",
		"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-e4b

SGLang

How to use evalengine/unbound-e4b 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-e4b" \
    --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-e4b",
		"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-e4b" \
        --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-e4b",
		"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-e4b with Docker Model Runner:
```
docker model run hf.co/evalengine/unbound-e4b
```

johnsonchromia commited on 1 day ago

Commit

5cbfb31

verified ·

1 Parent(s): 0ce9060

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

Browse files

Files changed (1) hide show

README.md +11 -3

README.md CHANGED Viewed

@@ -42,8 +42,16 @@ are at [`evalengine/unbound-e4b-GGUF`](https://huggingface.co/evalengine/unbound
 | TruthfulQA mc2 (`--limit 100`) | 0.439 | 0.486 | +4.7 pt |
 | MMLU (`--limit 100`, 61 subtasks avg) | ~0.425 | 0.392 | −3.3 pt |
 | GSM8K (flexible-extract, `--limit 100`) | 0.74 (limit 200) | 0.58 | regression mostly limit-noise |
 | KL divergence vs base | 0 | 3.25 | (SFT-expected) |
 **vs Unbound E2B (current ship):** +8 pp useful-compliance, −3 pp
 hallucination, **~5× the GSM8K math score**, cleaner KL (3.25 vs 3.76).
 Refusal rate is essentially the same (~2.7%).
@@ -58,9 +66,9 @@ Refusal rate is essentially the same (~2.7%).
 ## Use
 ```bash
-# on-device (GGUF)
-ollama pull hf.co/evalengine/unbound-e4b-GGUF
-ollama run  hf.co/evalengine/unbound-e4b-GGUF
 ```
 ```python

 | TruthfulQA mc2 (`--limit 100`) | 0.439 | 0.486 | +4.7 pt |
 | MMLU (`--limit 100`, 61 subtasks avg) | ~0.425 | 0.392 | −3.3 pt |
 | GSM8K (flexible-extract, `--limit 100`) | 0.74 (limit 200) | 0.58 | regression mostly limit-noise |
+| GPQA-Diamond (`--limit 200`) | 25.25% | 25.76% | +0.5 pt (within stderr) |
+| BBH macro (24 tasks, `--limit 200`) | 54.26% | 53.45% | −0.8 pt (within stderr) |
 | KL divergence vs base | 0 | 3.25 | (SFT-expected) |
+GPQA-Diamond and BBH macro — the lm-eval-harness "release" suite at
+`--limit 200` — both land **within stderr of base**: E4B's larger capacity
+absorbs the SFT shift cleanly. The −3.3 pt MMLU dip on the limit-100 fast
+pass is at the edge of that suite's resolution and is not corroborated by
+the release pass.
 **vs Unbound E2B (current ship):** +8 pp useful-compliance, −3 pp
 hallucination, **~5× the GSM8K math score**, cleaner KL (3.25 vs 3.76).
 Refusal rate is essentially the same (~2.7%).
 ## Use
 ```bash
+# on-device (Ollama Registry — single-file Q4_K_M, identity-grounded Modelfile)
+ollama pull evalengine/unbound-e4b
+ollama run  evalengine/unbound-e4b
 ```
 ```python