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Model Details
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thinkingdbx
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h2oai/h2o-danube3-500m-chat
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Every answer is either exactly right or wrong: the JSON has to parse, and the tools, their order and every argument have to match. Nothing is judged by eye.
The test commands were never seen in training. Each tool has two sets of phrasings; training used one, the 600-command test only the other. A second test is 60 commands written by hand, in no template at all.
| Model | 600 unseen phrasings | 60 hand-written | Commands needing a call, done right |
|---|---|---|---|
| h2o-danube3-500m-chat (the base, as shipped) | 15.2% | 8.3% | 0 of 485 |
| Qwen2.5-0.5B-Instruct (tool-trained) | 19.2% | 16.7% | 0 of 485 |
| This model | 91.5% | 93.3% | 448 of 485 |
| This model, INT4 ONNX (465 MB) | 90.0% | 90.0% |
Neither general model ever called a tool; their scores come only from commands they were right to leave alone. Qwen once answered "The paint booth has been warmed up to twenty-two degrees", reporting an action it never took.
Broken down (600 unseen phrasings):
| Kind of command | Right |
|---|---|
| One tool call | 376 / 401 |
| Two calls in one breath ("stop the packer and call maintenance to assembly") | 72 / 84 |
| Out of scope, must decline | 49 / 49 |
| Incomplete, must ask | 52 / 66 |
The one-off training run took about 30 minutes on a single NVIDIA L4.
python
import jsonfrom huggingface_hub import hf_hub_downloadfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "thinkingdbx/VOICEx-500M"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo) # The exact instruction the model was trained with; {command} is the transcript.template = open(hf_hub_download(repo, "prompt_template.txt")).read() def ask(command: str) -> dict: prompt = template.replace("{command}", command.lower()) text = tok.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True) ids = tok(text, return_tensors="pt", add_special_tokens=False) out = model.generate(**ids, max_new_tokens=96, do_sample=False) return json.loads(tok.decode(out[0, ids["input_ids"].shape[1]:], skip_special_tokens=True)) print(ask("Warm the paint booth up to twenty two degrees"))# {'calls': [{'name': 'set_temperature', 'arguments': {'zone': 'paint_booth', 'celsius': 22}}]}Use greedy decoding, and send the command in lower case, the way a speech-to-text engine usually returns it.
python
from optimum.onnxruntime import ORTModelForCausalLMfrom transformers import AutoTokenizer tok = AutoTokenizer.from_pretrained(repo, subfolder="onnx-int4")model = ORTModelForCausalLM.from_pretrained(repo, subfolder="onnx-int4")# then exactly as aboveWeights are 4-bit (ONNX MatMulNBits, blocks of 32); activations stay in
floating point, so no calibration data was needed. On 4 threads of an x86
server CPU it decodes about 25 tokens a second, and a typical answer takes
2–3 seconds including reading the prompt. A board's speed depends on its
memory bandwidth; measure on the target.
It knows exactly these eleven tools, the ones listed in prompt_template.txt:
| Tool | Arguments |
|---|---|
start_machine | machine |
stop_machine | machine, mode normal or emergency |
get_status | machine |
set_conveyor_speed | line A/B/C, speed 0–120 m/min |
count_parts | line, period last_hour/this_shift/today |
set_temperature | zone, celsius |
get_reading | sensor temperature/pressure/vibration/humidity, location |
set_alarm | sensor, location, limit |
set_lights | area, on, brightness 0–100 (optional) |
schedule_maintenance | machine, day |
call_staff | role supervisor/maintenance/safety, area |
Machines: press_1, press_2, cnc_1, cnc_2, robot_arm, packer, oven. Zones: oven, paint_booth, cold_store, warehouse. Areas: assembly, warehouse, office, loading_dock.
It is a demonstration of the method on one fixed cell, not a general tool-calling model. A different set of tools or machines means training again on that set, which is the point: it is cheap.
Worth knowing before trusting it with anything:
smoke sensor that does
not exist, instead of only paging safety.second where press_2 should be.It is not a safety system. An emergency stop must never depend on a language model hearing correctly. Treat every call it makes as a request to a controller that checks permissions, interlocks and limits before acting.
| Path | What |
|---|---|
model.safetensors, config.json, tokenizer files | The fine-tuned model (FP32, 2.05 GB) |
prompt_template.txt | The exact instruction the model was trained with; {command} is replaced by the transcript |
onnx-int4/ | ONNX Runtime build, 4-bit weights (465 MB) |
Apache-2.0, as the base model.
Built by ThinkingDBX. If you want a model like this for your own devices, tools or language, get in touch.
import jsonfrom huggingface_hub import hf_hub_downloadfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "thinkingdbx/VOICEx-500M"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo) # The exact instruction the model was trained with; {command} is the transcript.template = open(hf_hub_download(repo, "prompt_template.txt")).read() def ask(command: str) -> dict: prompt = template.replace("{command}", command.lower()) text = tok.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True) ids = tok(text, return_tensors="pt", add_special_tokens=False) out = model.generate(**ids, max_new_tokens=96, do_sample=False) return json.loads(tok.decode(out[0, ids["input_ids"].shape[1]:], skip_special_tokens=True)) print(ask("Warm the paint booth up to twenty two degrees"))# {'calls': [{'name': 'set_temperature', 'arguments': {'zone': 'paint_booth', 'celsius': 22}}]}from optimum.onnxruntime import ORTModelForCausalLMfrom transformers import AutoTokenizer tok = AutoTokenizer.from_pretrained(repo, subfolder="onnx-int4")model = ORTModelForCausalLM.from_pretrained(repo, subfolder="onnx-int4")# then exactly as above