README.md

---
base_model: unsloth/gemma-4-E4B-it-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
language:
- en
tags:
- base_model:adapter:unsloth/gemma-4-E4B-it-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
- gemma
- android
- adb
- function-calling
- tool-use
---

# DroidADB: Gemma 4 E4B LoRA for Android/ADB Tool Calling

DroidADB is a LoRA adapter for Gemma 4 E4B Instruct. It chats normally, and when a request involves Android or ADB it calls an `adb` function tool instead of just printing a command. This is a personal project, trained on a personal Google Colab T4 GPU. It isn't affiliated with or funded by any organization.

## Model Details

### Model Description

This adapter was trained with supervised fine-tuning (SFT) using Unsloth and TRL. It uses Gemma 4's native function-calling format. Each training conversation has this shape:

user request → `adb` tool call → tool result → final assistant reply

A small set of general chat examples was mixed in so the model can still hold a normal conversation.

- **Developed by:** Muneesh Kanimbakam
- **Funded by:** Self-funded (personal project)
- **Shared by:** Muneesh Kanimbakam
- **Model type:** Causal LM, LoRA adapter (PEFT), function-calling / tool-use
- **Language(s):** English
- **License:** <TODO: must match the base Gemma 4 license>
- **Finetuned from model:** `unsloth/gemma-4-E4B-it`, loaded in 4-bit (resolved to `unsloth/gemma-4-E4B-it-unsloth-bnb-4bit`)

### Model Sources

- **Repository:** https://huggingface.co/<HF_USERNAME>/<MODEL_NAME>

## Uses

### Direct Use

- Turning natural-language Android requests into `adb` tool calls, e.g. "list installed packages", "show device logs", "get the battery status"
- General conversation, such as simple explanations, planning and summaries
- Serving as the model in a host app's tool loop: the host runs the tool and the model answers using the tool result

### Downstream Use

- Plugging into any agent framework that supports OpenAI/Gemma-style function tools
- Adding more tools through a tool registry (`register_tool`) without retraining
- Exporting to GGUF (`q4_k_m`) for llama.cpp or Ollama

### Out-of-Scope Use

- Running ADB commands automatically without a human approving them
- Rooting a device, modifying system partitions, or bypassing device security
- Production device management, or any high-stakes or safety-critical use
- Non-Android technical tasks that need expert accuracy

## Bias, Risks, and Limitations

- **Risk of wrong commands:** the model may produce ADB commands that are incorrect, outdated, or harmful. Always review a command before running it.
- **The adapter itself doesn't execute anything.** Safety comes from the host runtime. The reference runtime in the notebook:
  - dry-runs by default (`ALLOW_COMMAND_EXECUTION = False`)
  - accepts only commands that start with `adb`
  - blocks shell operators (`|`, `;`, `&&`, `||`, `>`, `>>`) and runs without `shell=True`
  - asks for confirmation before risky commands (`install`, `uninstall`, `rm`, `mv`, `reboot`, `settings put`, `chmod`, `chown`, `su`, `wipe`, `format`, `pm clear`)
- **Synthetic tool results:** in training, every tool result was a placeholder that said the command was not executed. The model never saw real device output, so it may handle real outputs poorly.
- **Limited general chat:** only 4 general-chat examples were included, so conversational quality relies mostly on the base model.
- Inherits the biases and limitations of the base Gemma model. 4-bit quantization may slightly reduce quality.

### Recommendations

Keep execution turned off by default. Require a human to approve each command, and pin the target device (`ADB_SERIAL`). Prefer read-only diagnostic commands.

## How to Get Started with the Model

```python
from unsloth import FastModel
from unsloth.chat_templates import get_chat_template

model, tokenizer = FastModel.from_pretrained(
    model_name=""Muneeshk/gemma-4-adb-agent"",
    max_seq_length=4096,
    load_in_4bit=True,
)
tokenizer = get_chat_template(tokenizer, chat_template="gemma-4")
FastModel.for_inference(model)

ADB_TOOL = {
    "type": "function",
    "function": {
        "name": "adb",
        "description": "Run one Android Debug Bridge command on an explicitly selected device.",
        "parameters": {
            "type": "object",
            "properties": {
                "command": {"type": "string", "description": "Full command beginning with adb. Never include a shell pipeline."},
                "confirm": {"type": "boolean", "description": "Set true only after user explicitly confirms a risky command."},
            },
            "required": ["command"],
        },
    },
}

messages = [
    {"role": "system", "content": "You are DroidADB, a friendly general-purpose assistant with strong Android and ADB expertise."},
    {"role": "user", "content": "How do I list all installed Android packages?"},
]
inputs = tokenizer.apply_chat_template(
    messages, tools=[ADB_TOOL], tokenize=True, add_generation_prompt=True,
    return_dict=True, return_tensors="pt",
).to(model.device)

out = model.generate(**inputs, max_new_tokens=256, do_sa**le=True,
                     tem**rature=1.0, top_p=0.95, top_k=64,**se_cache=True)
print(tokenizer.de**de(out[0][inputs["input_ids"].shape[-1\]:], skip_special_tokens=Fals**)