Instructions to use AIcell/Guava-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIcell/Guava-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="AIcell/Guava-4B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AIcell/Guava-4B")
model = AutoModelForMultimodalLM.from_pretrained("AIcell/Guava-4B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIcell/Guava-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIcell/Guava-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIcell/Guava-4B",
		"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/AIcell/Guava-4B

SGLang

How to use AIcell/Guava-4B 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 "AIcell/Guava-4B" \
    --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": "AIcell/Guava-4B",
		"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 "AIcell/Guava-4B" \
        --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": "AIcell/Guava-4B",
		"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 AIcell/Guava-4B with Docker Model Runner:
```
docker model run hf.co/AIcell/Guava-4B
```

Guava-4B / README.md

AIcell

Update README.md

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---
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - robotics
  - vision-language-action
  - manipulation
  - qwen3_5
---
# guava-v13b-qwen3.5-4b

A Qwen3.5-VL 4B model fine-tuned on the **Guava SFT data** robot-manipulation dataset
(2,268 episodes across 16 tabletop tasks). 

Training data: [AIcell/Guava-4B-SFT-data](https://huggingface.co/datasets/AIcell/Guava-4B-SFT-data).

## Model

| | |
|---|---|
| Architecture | `Qwen3_5ForConditionalGeneration` (vision + text) |
| Precision | bfloat16 |
| Hidden size | 2560, 32 layers (linear attention, full attention every 4th) |
| Vocab | 248,320 |
| Max positions | 262,144 |
| Vision | 24-layer ViT, patch 16, merge 2 |
| Shards | 2 safetensors, 8.47 GiB total |

## Usage

```python
from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained(
    "AIcell/Guava-4B", dtype="bfloat16", device_map="auto"
)
processor = AutoProcessor.from_pretrained("AIcell/Guava-4B")
```

Episodes in the training data contain up to 30 images, and coordinates are already
table-aligned (tabletop is z = 0) — do not apply table-height normalization a second time.

## Tasks

`apple_juice_order`, `bread_near_lemon`, `can_in_bin`, `close_drawer`, `cube_stack`,
`cube_under_cup`, `hotdog_near_donut`, `milk_near_cup`, `open_drawer`, `pick_up_orange`,
`push_basket`, `push_cereal`, `red_objects_in_basket`, `remove_cube_from_tray`,
`shell_game`, `tomato_in_bowl`

	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- robotics
	- vision-language-action
	- manipulation
	- qwen3_5
	---

	# guava-v13b-qwen3.5-4b

	A Qwen3.5-VL 4B model fine-tuned on the Guava SFT data robot-manipulation dataset
	(2,268 episodes across 16 tabletop tasks).

	Training data: [AIcell/Guava-4B-SFT-data](https://huggingface.co/datasets/AIcell/Guava-4B-SFT-data).

	## Model

	\| \| \|
	\|---\|---\|
	\| Architecture \| `Qwen3_5ForConditionalGeneration` (vision + text) \|
	\| Precision \| bfloat16 \|
	\| Hidden size \| 2560, 32 layers (linear attention, full attention every 4th) \|
	\| Vocab \| 248,320 \|
	\| Max positions \| 262,144 \|
	\| Vision \| 24-layer ViT, patch 16, merge 2 \|
	\| Shards \| 2 safetensors, 8.47 GiB total \|

	## Usage

	```python
	from transformers import AutoModelForImageTextToText, AutoProcessor

	model = AutoModelForImageTextToText.from_pretrained(
	"AIcell/Guava-4B", dtype="bfloat16", device_map="auto"
	)
	processor = AutoProcessor.from_pretrained("AIcell/Guava-4B")
	```

	Episodes in the training data contain up to 30 images, and coordinates are already
	table-aligned (tabletop is z = 0) — do not apply table-height normalization a second time.

	## Tasks

	`apple_juice_order`, `bread_near_lemon`, `can_in_bin`, `close_drawer`, `cube_stack`,
	`cube_under_cup`, `hotdog_near_donut`, `milk_near_cup`, `open_drawer`, `pick_up_orange`,
	`push_basket`, `push_cereal`, `red_objects_in_basket`, `remove_cube_from_tray`,
	`shell_game`, `tomato_in_bowl`