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StarVLA/
Qwen3-VL-4B-Instruct-Action

Name: Qwen3-VL-4B-Instruct-Action
Availability: InStock

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qwen3_vl

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Qwen3-VL-4B-Instruct with action tokens

This repository is a Qwen3-VL-4B-Instruct initializer whose tokenizer and text vocabulary have been extended with 2,048 tokens named <robot_action_0> through <robot_action_2047>. It is intended as a starting point for StarVLA frameworks that discretize robot actions as tokens; it is not a trained robot policy.

Model and input contract

Item	Value
Base model	Qwen/Qwen3-VL-4B-Instruct
Architecture	`Qwen3VLForConditionalGeneration`
Added action tokens	2,048
Action-token IDs	151669–153716 in the packaged tokenizer
Inputs	Same text, image, and video inputs as the base Qwen3-VL model
Output	Language-model logits over the extended vocabulary
Robot action head	None
Robot checkpoint	None

The extension was produced with starVLA/model/modules/vlm/tools/add_qwen_special_tokens.

Training and evaluation

No additional VLM or robot-policy fine-tuning is reported for this repository. The pre-existing Qwen3-VL parameters are retained apart from resizing the token embedding/output vocabulary for the new entries. The added action-token embeddings therefore should be treated as initialization, not as learned action semantics.

No robot benchmark result is associated with this repository.

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Use the model and tokenizer from the same repository:

python

from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

repo_id = "StarVLA/Qwen3-VL-4B-Instruct-Action"
processor = AutoProcessor.from_pretrained(repo_id)
model = Qwen3VLForConditionalGeneration.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
)

Downstream training must define the action quantization/codebook that maps continuous robot actions to the 2,048 token indices.

Evidence and limitations

config.json verifies Qwen3VLForConditionalGeneration; added_tokens.json verifies all 2,048 action-token entries.
This repository does not define normalization, action dimension, action horizon, robot embodiment, dataset, controller, or evaluation protocol.
Token IDs are repository-specific. Mixing this tokenizer with the unextended base model or another action-token checkpoint can silently corrupt decoding.
The model inherits the base model license, acceptable-use requirements, biases, and failure modes.