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README.md

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----
-license: apache-2.0
----
+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+This repository contains the model for the paper "Advancing Mobile GUI Agents: A Verifier-Driven Approach to Practical Deployment" (arXiv:2503.15937).
+
+V-Droid is a novel mobile GUI task automation agent that leverages Large Language Models (LLMs) as verifiers rather than generators. This verifier-driven paradigm, combined with a discretized action space and a prefilling-only workflow, allows V-Droid to achieve state-of-the-art performance on public benchmarks while maintaining near-real-time decision-making capabilities.
+
+## Model Details
+
+### Model Description
+
+V-Droid is a mobile GUI agent designed to automate tasks on Android devices. Unlike conventional approaches where LLMs generate actions directly, V-Droid first identifies a set of candidate actions from the current screen and then uses a verifier model to evaluate and select the most promising action. This approach significantly improves accuracy and reduces latency.
+
+The core components of the V-Droid framework include:
+1. Discretized Action Space: V-Droid operates on a defined set of actions (e.g., click, type), enabling efficient identification of potential actions on a given screen.
+2. Verifier-based Decision Making: An LLM-based verifier assesses the utility of each candidate action in the context of the current task, leading to more reliable decisions.
+3. Pair-wise Progress Preference Training (P3T): A novel training strategy that enhances the verifier's ability to distinguish between beneficial and detrimental actions.
+4. Human-Agent Joint Annotation: A scalable and efficient method for collecting high-quality training data.
+
+This model card corresponds to the verifier component of the V-Droid agent.
+
+- **Developed by:** Gaole Dai, Shiqi Jiang, Ting Cao, Yuanchun Li, Yuqing Yang, Rui Tan, Mo Li, Lili Qiu
+- **Model type:** Verifier for Mobile GUI Agent
+- **Language(s) (NLP):** English
+- **Finetuned from model [optional]:** Llama-3.1-8B-4bit
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/V-Droid-Agent/V-Droid-Public
+- **Paper:** https://doi.org/10.48550/arXiv.2503.15937
+- **Demo:** https://v-droid-agent.github.io/
+
+## Uses
+
+### Direct Use
+
+The V-Droid verifier model is intended to be used as a core component within the V-Droid agent framework. It takes a HTML description of a mobile device, a task description, and a candidate action as input, and outputs a score that the action will contribute to completing the task.
+
+### Downstream Use [optional]
+
+The principles and architecture of V-Droid can be adapted for other GUI automation tasks beyond mobile environments, such as web or desktop applications. The verifier-driven approach could also be explored for other types of autonomous agents where action validation is critical.
+
+### Out-of-Scope Use
+
+This model is not designed for generating free-form text or engaging in conversational chat. It is specifically tailored for the task of action verification in a mobile GUI context. Using it for purposes outside of this scope is likely to yield poor results. The model should not be used for any applications that could cause harm, violate privacy, or conduct malicious activities.
+
+## Bias, Risks, and Limitations
+
+The performance of V-Droid is dependent on the quality and diversity of the training data. As with any model trained on specific data distributions, it may exhibit biases present in the training set. The model's ability to generalize to unseen applications or radically different UI layouts may be limited.
+
+### Recommendations
+
+Users should be aware of the potential for errors and biases. For critical tasks, human oversight is recommended. To improve performance on specific applications, fine-tuning the verifier on a targeted dataset of interactions with those applications would be beneficial.
+
+## Training Details
+
+### Training Data
+
+V-Droid was trained on data collected through a human-agent joint annotation process. This involved human annotators interacting with V-Droid, correcting its mistakes, and providing demonstrations of successful task completion. The paper does not point to a specific public dataset release at this time.
+
+### Training Procedure
+
+The training data consists of state-action pairs, where the state includes the screen information and the task description. Actions are categorized as either positive (contributing to task completion) or negative.
+
+## Citation
+**BibTeX:**
+  @article{dai2025advancing,
+    title={Advancing Mobile GUI Agents: A Verifier-Driven Approach to Practical Deployment},
+    author={Dai, Gaole and Jiang, Shiqi and Cao, Ting and Li, Yuanchun and Yang, Yuqing and Tan, Rui and Li, Mo and Qiu, Lili},
+    journal={arXiv preprint arXiv:2503.15937},
+    year={2025}
+  }

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+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "padding_side": "left",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": null
+}

v_head.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4182386e65821e18dc67f8434abe606dd2df434e3870e78313af0118f37a197b
+size 257623