Instructions to use dazhiga/v5_act_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dazhiga/v5_act_only with Transformers:

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

pipe = pipeline("image-text-to-text", model="dazhiga/v5_act_only")
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("dazhiga/v5_act_only")
model = AutoModelForMultimodalLM.from_pretrained("dazhiga/v5_act_only", 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 dazhiga/v5_act_only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dazhiga/v5_act_only"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dazhiga/v5_act_only",
		"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/dazhiga/v5_act_only

SGLang

How to use dazhiga/v5_act_only 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 "dazhiga/v5_act_only" \
    --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": "dazhiga/v5_act_only",
		"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 "dazhiga/v5_act_only" \
        --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": "dazhiga/v5_act_only",
		"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 dazhiga/v5_act_only with Docker Model Runner:
```
docker model run hf.co/dazhiga/v5_act_only
```

v5_act_only

Action-only ablation model of the JarvisVLA (Minecraft VLA) v5 pipeline, trained with flash-attn.

Base model: JarvisVLA-Qwen2-VL-7B (Qwen2-VL 7B backbone, full-parameter tuning with ViT and aligner frozen)
Training data: the action (VLA) split of the v5 stage2 dataset — 297,270 frames from 4,706 complete Minecraft trajectories (mine_block / kill_entity / craft_item), single-turn three-frame flat format, no text-QA rows
Loss: ordinary cross-entropy on the current assistant turn (action tokens), loss_scale=last_round
Hyperparameters: 1,162 steps (~1 epoch), global batch 256 (8 GPUs x 2 x 16), lr 1e-5 cosine, warmup 50, bf16, DeepSpeed ZeRO2, max_length 8192, flash-attn 2.8.3 attention
Inputs: instruction + last 2 frames + current frame (3 images); Outputs: reserved action tokens (ids 151835-151907) that decode to Minecraft keyboard/camera actions

Intended for research on vision-language-action models and action-only vs mixed action+QA training ablations.

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16