Model: ReXeeD/TASX-Cmd-0.5B-GGUF Source: Original Platform
language, tags, base_model, pipeline_tag, license
| language | tags | base_model | pipeline_tag | license | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
ReXeeD/TASX-Cmd-0.5B | text-generation | apache-2.0 |
TASX-Command-0.5B
TASX-Command-0.5B is a highly specialized, lightweight language model designed specifically for robotics. It translates natural language (including slang, typos, and complex phrasing) into strict, execution-ready JSON command sequences for ROS2, SLAM, and physical robot control.
By fine-tuning the Qwen2.5-0.5B base model, we created a "robot brain" that is small and fast enough to run locally on edge hardware (like a Raspberry Pi) via llama.cpp while retaining the intelligence to understand complex human intent.
Quantized Versions (GGUF)
For high-performance inference , use these GGUF quants:
- TASX-Cmd-0.5B-GGUF (mradermacher) — Includes high-quality iMatrix and IQ quants.
Key Features
- Strict JSON Output: Never outputs conversational filler; only valid JSON arrays.
- Typo & Slang Immunity: Successfully maps messy speech (e.g., "scoot forward lik 3 point 5 meeters") to perfect floats and commands.
- Dynamic Location Extraction: Converts any spoken room or location name (e.g., "Professor Xavier's Office") into clean
snake_case(e.g.,professor_xavier_office). - Physical Constraint Logic: Automatically generates implicit macro sequences (like
sit->stand->move) for fetching and delivering items without needing explicit user instruction.
Supported Actions & Commands
The model is trained to strictly output one or more of the following 20 commands formatted as a JSON array of actions.
1. Teleop (Movement & Speed)
{"type": "teleop", "cmd": "move_forward", "distance": <float>}{"type": "teleop", "cmd": "move_backward", "distance": <float>}{"type": "teleop", "cmd": "rotate_left", "angle": <float>}{"type": "teleop", "cmd": "rotate_right", "angle": <float>}{"type": "teleop", "cmd": "set_speed", "level": "slow" | "normal" | "fast"}{"type": "teleop", "cmd": "stop"}(For casual pauses){"type": "teleop", "cmd": "e_stop"}(For panicked/emergency stops)
2. Nav2 (Autonomous Navigation)
{"type": "nav2", "cmd": "go_to_waypoint", "target": "<snake_case_string>"}{"type": "nav2", "cmd": "cancel_goal"}
3. Stunts (Posture & Tricks)
{"type": "stunt", "cmd": "full_sit"}{"type": "stunt", "cmd": "half_sit"}{"type": "stunt", "cmd": "stand_up"}{"type": "stunt", "cmd": "spin", "direction": "clockwise" | "anticlockwise"}
Advanced Behaviors (Macros)
TASX-Command-0.5B has been taught physical robotics logic. It knows a robot cannot drive while sitting.
If you ask it to perform a delivery (e.g., "Fetch my laptop from the server room and bring it to John's desk"), it will automatically output the required posture macros:
{
"actions": [
{"type": "nav2", "cmd": "go_to_waypoint", "target": "server_room"},
{"type": "stunt", "cmd": "full_sit"},
{"type": "stunt", "cmd": "stand_up"},
{"type": "nav2", "cmd": "go_to_waypoint", "target": "john_desk"},
{"type": "stunt", "cmd": "full_sit"}
]
}
Test Script
from llama_cpp import Llama
print("⏳ Loading model... please wait.")
llm = Llama(
model_path="./tasx_sft_merged_gguf/tasx_sft_merged.Q8_0.gguf",
n_ctx=512,
stop=["<|im_end|>"],
verbose=False
)
print("\n" + "="*50)
print("TASX ROBOT ")
print("Type a command and press Enter. Type 'q' to quit.")
print("="*50 + "\n")
while True:
user_text = input("🎤 You: ")
if user_text.lower() in ['q', 'quit', 'exit']:
print("Stopping tester. Great job!")
break
if not user_text.strip():
continue
prompt = f"<|im_start|>user\n{user_text}<|im_end|>\n<|im_start|>assistant\n"
output = llm(
prompt,
max_tokens=150,
temperature=0,
echo=False
)
response = output["choices"][0]["text"].strip()
print(f"TASX: {response}\n")
Contact
Need a custom version of this model for your specific robot's API or hardware? Contact: [albinthomas7034@gmail.com]