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Model: ReXeeD/TASX-Cmd-0.5B-GGUF Source: Original Platform
2026-06-20 11:51:18 +08:00
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+---
+language:
+- en
+tags:
+- robotics
+- ros2
+- json
+- unsloth
+- qwen
+- text-generation
+- hardware
+base_model: ReXeeD/TASX-Cmd-0.5B
+pipeline_tag: text-generation
+license: 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)](https://huggingface.co/mradermacher/TASX-Cmd-0.5B-GGUF)** — *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:
+```json
+{
+  "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]