Instructions to use gnitoahc/ceed-b4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gnitoahc/ceed-b4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="gnitoahc/ceed-b4")
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("gnitoahc/ceed-b4")
model = AutoModelForMultimodalLM.from_pretrained("gnitoahc/ceed-b4", 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 gnitoahc/ceed-b4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gnitoahc/ceed-b4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gnitoahc/ceed-b4",
		"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/gnitoahc/ceed-b4

SGLang

How to use gnitoahc/ceed-b4 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 "gnitoahc/ceed-b4" \
    --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": "gnitoahc/ceed-b4",
		"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 "gnitoahc/ceed-b4" \
        --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": "gnitoahc/ceed-b4",
		"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 gnitoahc/ceed-b4 with Docker Model Runner:
```
docker model run hf.co/gnitoahc/ceed-b4
```

ceed-b4 / README.md

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	---
	license: gemma
	base_model: google/gemma-4-e4b-it
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- vision-language
	- visual-question-answering
	- knowledge-distillation
	- lora
	- merged
	- research
	datasets:
	- lmms-lab/DocVQA
	- lmms-lab/GQA
	- lmms-lab/ChartQA
	language:
	- en
	---

	# CEED B4 — gemma-4-e4b-it, distilled, with visual-advantage reweighting

	A LoRA fine-tune of [`google/gemma-4-e4b-it`](https://huggingface.co/google/gemma-4-e4b-it)
	trained with B2's objective plus VA-OPD's visual-advantage reweighting (arXiv:2605.21924).
	The teacher is the sparse mixture-of-experts [`google/gemma-4-26b-a4b-it`](https://huggingface.co/google/gemma-4-26b-a4b-it).

	The adapter has been folded into the base weights, so this is a standalone
	checkpoint: load it exactly like the base model, with no PEFT and no CEED code.

	This is Group B4 of the CEED study (Causal Expert–Evidence Distillation),
	a research artifact published for reproducibility. It is not a product.

	## Usage

	```python
	from transformers import AutoModelForImageTextToText, AutoProcessor

	model = AutoModelForImageTextToText.from_pretrained("gnitoahc/ceed-b4", dtype="float16")
	processor = AutoProcessor.from_pretrained("gnitoahc/ceed-b4")
	```

	The model was trained and scored with a short-answer instruction in the prompt.
	Without it an instruction-tuned model answers `"The total written in the image is
	28."` against gold `"28"` and scores zero on every metric here.

	## Training

	\| \| \|
	\| --- \| --- \|
	\| Corpus \| chartqa 2,500, docvqa 5,349, gqa 10,000 (17,849 examples, 80/10/10 split by example id) \|
	\| Passes over the training split \| 2.69 \|
	\| Adapter \| LoRA rank 4 \|
	\| Final cross-entropy \| 1.0979 \|
	\| Final KD term \| 2.1309 \|
	\| Seed \| 0 \|
	\| Run identity \| `6a4544ff1af675be8b364825a3f3f5c7bd825801471267ebd5f4956ff3febaa8` \|

	## Evaluation

	\| Dataset \| Metric \| Score \| n \|
	\| --- \| --- \| --- \| --- \|
	\| docvqa \| ANLS \| 0.8538 \| 565 \|
	\| gqa \| exact match \| 0.6102 \| 1016 \|
	\| chartqa \| relaxed accuracy \| 0.6185 \| 249 \|

	Scored by CEED's own harness (`harness_version: ceed-direct-1`)
	with greedy decoding, on CEED's own 10% validation split.

	**These numbers are not comparable to published DocVQA / GQA / ChartQA leaderboard
	results.** Different splits, different prompt, different decoding. They are
	meaningful only against the other CEED Groups, which were scored identically.

	## Limitations

	- This is a LoRA result. Merging folds the adapter into the weights; it does
	not turn a rank-4 adapter into a full
	fine-tune. CEED's own ADR-0005 bars LoRA numbers from the study's headline
	table, because a null result under a small adapter cannot be attributed between
	"the signal does not transfer" and "the adapter lacked the capacity to hold it".
	Read any comparison involving this checkpoint with that in mind.
	- The distillation gain is not established. The no-teacher control (CEED B1), trained identically but with `kd_weight: 0`, scored above this checkpoint on every dataset (docvqa 0.8538 vs 0.8798; gqa 0.6102 vs 0.6959; chartqa 0.6185 vs 0.7871). Whatever this checkpoint's objective contributes, it is not visible as an advantage over supervised fine-tuning here.
	- Trained on document, natural-image and chart VQA in English only. Behaviour
	outside that is untested.
	- Inherits the base model's limitations and the Gemma licence.

	`ceed_provenance.json` beside the weights carries the source run's identity,
	parameter-efficiency mode, and metrics.