---
license: gemma
base_model: unsloth/gemma-3-270m-it
language:
- en
pipeline_tag: text-generation
tags:
- robotics
- text-to-json
- instruction-following
- mujoco
- gemma3
library_name: transformers
---

# LLM-Tank — Gemma-3 270M → robot JSON

Source-code: https://codeberg.org/imperius/llm-tank

Fine-tuned **Gemma-3 270M** that translates **one free-form English
instruction** for a tracked robot with a gripper arm into a strict JSON
command list, executed in a **MuJoCo** simulation.

Full pipeline: `text → this model → valid JSON → controller → robot
drives / grasps`. Code & sim: see the source repository.

![LLM-Tank demo](demo.gif)

## What it outputs

A single JSON object `{"commands": [ ... ]}`. Actions:

- `move` — `direction` (forward|backward), `distance_m`, `speed?`
- `turn` — `direction` (left|right), `angle_deg`, `speed?`
- `stop`, `wait` — `duration_s`
- `grasp` / `release` — optional `cell` ∈
  `front|front_left|front_right|left|right` (discrete, relative to the
  robot; IK is solved by the controller, **not** the model)
- out-of-scope / nonsense → `{"commands": []}`

The model emits **no coordinates** — only discrete actions/enums (this
keeps generation reliable and schema-checkable).

## Required input format (IMPORTANT)

The model was trained `train == infer` with a **fixed short system
prompt** folded with the instruction into ONE user turn. You must use
exactly this:

```python
import json
from transformers import AutoModelForCausalLM, AutoTokenizer

SYSTEM = ("You translate ONE English instruction for a tracked robot "
          "with a gripper arm into a single JSON object "
          '{"commands":[...]} using actions: move, turn, stop, wait, '
          "grasp, release. Output ONLY the JSON object, no prose, no "
          'markdown. If the instruction is out of scope or nonsense, '
          'output {"commands": []}.')

tok = AutoTokenizer.from_pretrained("PATH_OR_REPO")
model = AutoModelForCausalLM.from_pretrained("PATH_OR_REPO",
                                             torch_dtype="auto",
                                             device_map="auto")

def translate(instruction: str) -> dict:
    user = SYSTEM + "\n\n---\nINSTRUCTION: " + instruction.strip()
    enc = tok.apply_chat_template(
        [{"role": "user", "content": user}],
        tokenize=True, add_generation_prompt=True,
        return_dict=True, return_tensors="pt").to(model.device)
    out = model.generate(**enc, max_new_tokens=160, do_sample=False)
    txt = tok.decode(out[0][enc["input_ids"].shape[1]:],
                     skip_special_tokens=True)
    i, j = txt.find("{"), txt.rfind("}")
    try:
        return json.loads(txt[i:j + 1])
    except Exception:
        return {"commands": []}  # safe fallback

print(translate("go forward 2 meters then turn left"))
# {"commands": [{"action": "move", "direction": "forward",
#   "distance_m": 2.0}, {"action": "turn", "direction": "left",
#   "angle_deg": 90}]}
print(translate("pick it up"))      # {"commands": [{"action": "grasp"}]}
print(translate("make me a coffee"))# {"commands": []}
```

Greedy decoding (`do_sample=False`). The model is ~99% schema-valid
without constrained decoding; always keep the safe fallback.

## Metrics (held-out val, 352 examples: locomotion + manipulation + OOD)

| metric | value |
| --- | --- |
| schema_valid_rate | 0.991 |
| exact_match_rate | 0.943 |
| action_seq_accuracy | 0.980 |
| ood_f1 | 0.857 |
| task_success (MuJoCo, 40) | 0.975 |

## Training

Full fine-tuning (not LoRA) of `unsloth/gemma-3-270m-it` on ~3.5k
synthetic instruction→JSON pairs (generated with 120B models, validated
against a JSON Schema). fp32, Kaggle T4. Two phases: locomotion, then
+ arm (grasp/release). Details in the source repo (`docs/`).

## Demo

`demo.mp4` (in this repo) — ~1 min, two panes: left = command + model
JSON output, right = the robot acting in MuJoCo (real model + real
physics, not staged).

## Limitations

- No perception: the model can't target objects by name/color, only by
  discrete relative `cell`. Object resolution is spatial (controller
  grabs the nearest graspable body in the chosen cell).
- English only. Single fixed gripper, minimal custom arm.
- Designed for the accompanying controller/sim; raw JSON is meaningless
  without it.

## License

Weights are a derivative of Google **Gemma-3** — use is governed by the
[Gemma Terms of Use](https://ai.google.dev/gemma/terms). Accompanying
code is under its own license (see the source repository).