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Model: ReXeeD/TASX-Cmd-0.5B
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+tags:
+- robotics
+- ros2
+- json
+- unsloth
+- qwen
+- text-generation
+- hardware
+base_model: unsloth/Qwen2.5-0.5B-Instruct
+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.*
+* **[TASX-Cmd-0.5B-Q8_0 (ReXeeD)](https://huggingface.co/ReXeeD/TASX-Cmd-0.5B-GGUF)** — *Standard high-precision 8-bit quantization.*
+
+## 🌟 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 unsloth import FastLanguageModel
+import torch
+
+MODEL_PATH = "./tasx_sft_merged"
+MAX_SEQ_LENGTH = 512
+
+print("⏳ Loading your fine-tuned TASX model...")
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = MODEL_PATH,
+    max_seq_length = MAX_SEQ_LENGTH,
+    dtype = torch.float16, 
+    load_in_4bit = False,  
+)
+
+FastLanguageModel.for_inference(model) 
+
+print("\n" + "="*50)
+print("TASX ROBOT COMMAND TESTER READY")
+print("Type a command in the box below and press Enter.")
+print("Type 'quit' or 'q' to stop.")
+print("="*50 + "\n")
+
+
+while True:
+    user_text = input("🎤 You: ")
+    
+    if user_text.lower() in ['quit', 'exit', 'q']:
+        print("Stopping inference. Great job!")
+        break
+        
+    if not user_text.strip():
+        continue
+
+   
+    prompt = f"<|im_start|>user\n{user_text}<|im_end|>\n<|im_start|>assistant\n"
+    
+    
+    inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+
+    # Generate the output
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=150,
+        use_cache=True,
+        temperature=0.1,    
+        do_sample=True,
+    )
+
+    # Decode the output (slice off the prompt so we only see the assistant's new text)
+    response = tokenizer.batch_decode(
+        outputs[:, inputs.input_ids.shape[1]:], 
+        skip_special_tokens=True
+    )[0]
+    
+    print(f"TASX: {response.strip()}\n")
+```
+## Contact
+Need a custom version of this model for your specific robot's API or hardware?  Contact: [albinthomas7034@gmail.com]

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "float16",
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 896,
+    "initializer_range": 0.02,
+    "intermediate_size": 4864,
+    "layer_types": [
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+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 21,
+    "model_type": "qwen2",
+    "num_attention_heads": 14,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151665,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000.0,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.4.8",
+    "use_cache": false,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

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tokenizer.json

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tokenizer_config.json

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+  "backend": "tokenizers",
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+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+}