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GENERAL TERMS AND CONDITIONS
Note that if you want to use the Commercial licence, please contact us at contact@distillabs.ai
- Model License Terms -
R&D License
1. SERVICES, PRICES AND PAYMENT
1.1 The Customer pays a one-time license fee, as indicated in the check-out process, for running of one (1) training process of the selected Base Model using Customer Data (“License Fee”).
1.2 The License Fee shall be due for payment in advance. The Customer shall only be permitted to set off against payment claims of Distil Labs if the Customer’s claims are undisputed or have become res judicata.
2. MODEL LICENSE: R&D LICENSE
2.1 Subject to Customer’s payment of the license fee, Distil Labs grants to Customer the Model License (as defined below). For clarification, Distil Labs retains any other rights in its software or know- how, in particular in the codebase needed for the fine-tuning of the Trained Model.
2.2 Subject to the requirements of the Base Model License (cf. Section 2.5 below), Distil Labs transfers to the Customer the perpetual, non-exclusive usage right to the Trained Model for non-commercial purposes of prototyping and research & development. The Parties agree, that commercial purposes include deployment in production externally (to be used by Customer’s customers paid or free of charge) or internally (as a tool for Customer’s employees). The territorial scope of the license is limited to the use within the United States of America and the European Economic Area including all member states of the European Union (“Model License”).
2.3 The Model License for non-commercial purposes of prototyping and research & development shall include (i) the non-exclusive right to permanent or temporary reproduction, in whole or in part, by any means and in any form (e.g. permanent and/or volatile storage on electrical, electromagnetic, optical storage media, such as any type of SDD, HDD, DVD, memory cards, USB sticks), (ii) the non-exclusive right to distribution in any form, media and by any means regardless of whether the distribution is in tangible or intangible form, in particular to transmit the Trained Model via wired and wireless networks (e.g. for download from internet or intranet by wire or wireless means including broadband, cable, fiberglass, WIFI, LTE, 5G, satellite internet, other data networks), and (iii) the non-exclusive right of making available to the public in such a way that members of the public can access it from places and at times of their choice (e.g. by web or mobile app, virtual or augmented reality, cloud storage, cloud hosting, decentralized hosting, non-fungible token, application service providing, software as a service, or cloud computing). The license shall also contain, to the extent necessary for prototyping and research & development, the right to adapt and modify the Trained Model subject to the limitation in Section 2.4 and 2.5 below, to further develop the Trained Model including changes to functions or appearance, adapt to other software versions, to exchange parts of the Trained Model or combine the Trained Model with other results of work and to use the results in the same way as the original Trained Model. Any derived models from the Trained Model shall retain this model license.
2.4 The Customer shall not, without the prior written consent of Distil Labs:
2.4.1 train, fine-tune, re-train, or otherwise modify the Trained Model, unless for purpose of research & development;
2.4.2 use the Trained Model or any part thereof to create derivative models or services that compete with those of Distil Labs;
2.4.3 circumvent any technical restrictions embedded in the Trained Model or Base Model that are designed to enforce usage limitations.
2.5 The Parties acknowledge and agree that the Trained Model is developed from Base Models which are supplied by a third party. Therefore, the Model License is subject to the restrictions resulting from the open-source or any other applicable license of the Base Model (“Base Model License”) and the Customer must use the Trained Model in compliance with the Base Model License. In particular, the Customer must oblige their clients to compliance with the Base Model License in any case of transferring or sublicensing the rights to or making available in any way the Trained Model. The applicable Base Model License is defined in the Training Configuration and will be provided for download. The Customer agrees to indemnify Distil Labs for any and all claims brought by the Base Model provider for violations of the Base Model License.

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FROM ./model.gguf
TEMPLATE """{{- $lastUserIdx := -1 -}}
{{- range $idx, $msg := .Messages -}}
{{- if eq $msg.Role "user" }}{{ $lastUserIdx = $idx }}{{ end -}}
{{- end }}
{{- if or .System .Tools }}<|im_start|>system
{{ if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}
# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end -}}
<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if (and $.IsThinkSet (and .Thinking (or $last (gt $i $lastUserIdx)))) -}}
<think>{{ .Thinking }}</think>
{{ end -}}
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ if and $.IsThinkSet (not $.Think) -}}
<think>
</think>
{{ end -}}
{{ end }}
{{- end }}"""

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---
license: apache-2.0
base_model: Qwen/Qwen3-0.6B
tags:
- tool-calling
- smart-home
- function-calling
- distil-labs
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
# Distil-Home-Assistant-Qwen3
A fine-tuned Qwen3-0.6B model for multi-turn intent classification and slot extraction in an on-device smart home controller. Trained using knowledge distillation from a 120B teacher model, this 0.6B model delivers **96.7% tool call accuracy** — exceeding the teacher — while running locally on-device for private, low-latency smart home control.
For the GGUF version (for llama.cpp / Ollama deployment), see [distil-labs/distil-home-assistant-qwen3-gguf](https://huggingface.co/distil-labs/distil-home-assistant-qwen3-gguf).
## Results
| Model | Parameters | Tool Call Accuracy | ROUGE |
|---|:---:|:---:|:---:|
| GPT-oss-120B (teacher) | 120B | 94.1% | 98.2% |
| **This model (tuned)** | **0.6B** | **96.7%** | **99.2%** |
| Qwen3-0.6B (base) | 0.6B | — | — |
The fine-tuned model **exceeds the 120B teacher** on tool call accuracy while being **200x smaller**. Fine-tuning is essential for reliable multi-turn smart home conversations.
## Quick Start
### Using Transformers
```python
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("distil-labs/distil-home-assistant-qwen3")
tokenizer = AutoTokenizer.from_pretrained("distil-labs/distil-home-assistant-qwen3")
TOOLS = [
{"type": "function", "function": {"name": "toggle_lights", "description": "Turn lights on or off in a specified room", "parameters": {"type": "object", "properties": {"room": {"type": "string", "enum": ["living_room", "bedroom", "kitchen", "bathroom", "office", "hallway"]}, "state": {"type": "string", "enum": ["on", "off"]}}, "required": [], "additionalProperties": False}}},
{"type": "function", "function": {"name": "set_thermostat", "description": "Set the temperature for heating or cooling", "parameters": {"type": "object", "properties": {"temperature": {"type": "integer", "minimum": 60, "maximum": 80}, "mode": {"type": "string", "enum": ["heat", "cool", "auto"]}}, "required": [], "additionalProperties": False}}},
{"type": "function", "function": {"name": "lock_door", "description": "Lock or unlock a door", "parameters": {"type": "object", "properties": {"door": {"type": "string", "enum": ["front", "back", "garage", "side"]}, "state": {"type": "string", "enum": ["lock", "unlock"]}}, "required": [], "additionalProperties": False}}},
{"type": "function", "function": {"name": "get_device_status", "description": "Query the current state of a device or room", "parameters": {"type": "object", "properties": {"device_type": {"type": "string", "enum": ["lights", "thermostat", "door", "all"]}, "room": {"type": "string"}}, "required": [], "additionalProperties": False}}},
{"type": "function", "function": {"name": "set_scene", "description": "Activate a predefined scene", "parameters": {"type": "object", "properties": {"scene": {"type": "string", "enum": ["movie_night", "bedtime", "morning", "away", "party"]}}, "required": [], "additionalProperties": False}}},
{"type": "function", "function": {"name": "intent_unclear", "description": "Use when the user's intent cannot be determined", "parameters": {"type": "object", "properties": {"reason": {"type": "string", "enum": ["ambiguous", "off_topic", "incomplete", "unsupported_device"]}}, "required": [], "additionalProperties": False}}},
]
messages = [
{"role": "system", "content": "You are a tool-calling model working on:\n<task_description>You are an on-device smart home controller. Given a natural language command from the user, call the appropriate smart home function. If the user does not specify a required value (e.g. which room or what temperature), omit that parameter from the function call. Maintain context across conversation turns to resolve pronouns and sequential commands.</task_description>\n\nRespond to the conversation history by generating an appropriate tool call that satisfies the user request. Generate only the tool call according to the provided tool schema, do not generate anything else. Always respond with a tool call.\n\n"},
{"role": "user", "content": "Turn off the living room lights"},
]
text = tokenizer.apply_chat_template(
messages, tools=TOOLS, tokenize=False, add_generation_prompt=True,
enable_thinking=False,
)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
# <tool_call>
# {"name": "toggle_lights", "arguments": {"room": "living_room", "state": "off"}}
# </tool_call>
```
### Using Ollama
```bash
huggingface-cli download distil-labs/distil-home-assistant-qwen3 --local-dir distil-model
cd distil-model
ollama create distil-home-assistant -f Modelfile
ollama run distil-home-assistant
```
### Using with the Demo App
This model powers the [Smart Home Controller](https://github.com/distil-labs/distil-smart-home) demo — a text-based orchestrator that pairs an SLM with deterministic dialogue management for smart home control.
## Model Details
| Property | Value |
|---|---|
| Base Model | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) |
| Parameters | 0.6 billion |
| Architecture | Qwen3ForCausalLM |
| Context Length | 40,960 tokens |
| Precision | bfloat16 |
| Training Data | 50 seed conversations, synthetically expanded |
| Teacher Model | GPT-oss-120B |
| Task | Multi-turn tool calling (closed book) |
## Training
This model was trained using the [Distil Labs](https://distillabs.ai/?utm_source=huggingface&utm_medium=referral&utm_campaign=smart-home) platform:
1. **Seed Data**: 50 hand-written multi-turn conversations covering 6 smart home functions with 2-5 user turns per conversation
2. **Synthetic Expansion**: Expanded to thousands of examples using a 120B teacher model
3. **Fine-tuning**: Multi-turn tool calling distillation on Qwen3-0.6B
### What the Model Does
The model acts as a function caller for a smart home controller. Given a user command and conversation history, it outputs a structured tool call:
```
User: "Turn off the living room lights"
Model: {"name": "toggle_lights", "arguments": {"room": "living_room", "state": "off"}}
User: "The kitchen too"
Model: {"name": "toggle_lights", "arguments": {"room": "kitchen", "state": "off"}}
User: "Lock the front door"
Model: {"name": "lock_door", "arguments": {"door": "front", "state": "lock"}}
User: "Can you order me a pizza?"
Model: {"name": "intent_unclear", "arguments": {"reason": "off_topic"}}
```
### Supported Functions
The model handles 6 smart home operations:
| Function | Description |
|---|---|
| `toggle_lights` | Turn lights on or off in a room |
| `set_thermostat` | Set temperature and heating/cooling mode |
| `lock_door` | Lock or unlock a door |
| `get_device_status` | Query device state |
| `set_scene` | Activate a predefined scene |
| `intent_unclear` | Cannot determine intent |
## Use Cases
- On-device smart home controllers with privacy-first design
- Text-based smart home chatbots with structured intent routing
- Edge deployment for local smart home hubs
- Any multi-turn tool calling task with bounded intent taxonomy
## Limitations
- Trained on English smart home intents only
- Covers 6 specific smart home functions — not a general-purpose tool caller
- 96.7% accuracy means a small fraction of function calls may be incorrect
- Temperature range is fixed to 60-80°F
## License
This model is released under the Apache 2.0 license.
## Links
- [Demo App: Smart Home Controller](https://github.com/distil-labs/distil-smart-home)
- [Distil Labs Website](https://distillabs.ai/?utm_source=huggingface&utm_medium=referral&utm_campaign=smart-home)
- [GitHub](https://github.com/distil-labs)
- [Hugging Face](https://huggingface.co/distil-labs)
## Citation
```bibtex
@misc{distil-home-assistant-qwen3,
author = {Distil Labs},
title = {Distil-Home-Assistant-Qwen3: A Fine-tuned SLM for Smart Home Control},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/distil-labs/distil-home-assistant-qwen3}
}
```

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Copyright 2023 Qwen
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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Copyright 2025 OpenAI
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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{
"</think>": 151668,
"</tool_call>": 151658,
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"<|video_pad|>": 151656,
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"<|vision_start|>": 151652
}

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# 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' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- 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' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token": "<|endoftext|>",
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token": "<|im_end|>",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token": "<|endoftext|>",
"pad_token_id": 151643,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "4.57.0"
}

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size 1192135096

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{
"additional_special_tokens": [
"<|im_start|>",
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"<|object_ref_end|>",
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tokenizer_config.json Normal file
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