OrdenWills/LFM2.5-350M-home-assistant-sft

LFM2.5-350M Home Assistant ( Stable Release)

A purpose-trained smart home automation model fine-tuned from LiquidAI/LFM2.5-350M. This model controls lights, doors, thermostats, TVs, fans, speakers, and home scenes through structured tool calls, with full awareness of current device states.

⚠️ Version Notice: This is the Stable Release, trained exclusively on our robust State-Aware dataset (v13 Final Merge). The experimental multi-stage version is currently still in training and will be released for comparison once stabilized.

👉 Which file should I download? For the most stable experience right now, download one of the following GGUF files:

• LFM2.5-350M-home-assistant-sft.F16.gguf
• LFM2.5-350M-home-assistant-sft.Q4_K_M.gguf (Recommended for most users, best balance of speed and size)
• LFM2.5-350M-home-assistant-sft.Q8_0.gguf (Highest quality quantization)

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What It Does

Given a natural language command and the current state of all connected devices, the model outputs the correct tool call — or explains in plain text why no action is needed. It handles:

• Advanced Device Disambiguation — If you say "Turn off the TV", the model will intelligently resolve which TV you mean by checking if only one TV is connected, checking if you are in a room with a TV, or inferring intent from the state (e.g., if only one TV is currently ON, it turns that one off via a <think> trace).
• Media & Music Playback — "Play Truth In The World By Lucky Dube" → control_speaker(room='living_room', action='play', media='Truth In The World By Lucky Dube')
• Direct commands — "Turn on the bedroom light" → toggle_lights(room='bedroom', state='on')
• Already-satisfied detection — "Turn on the bedroom light" when bedroom:on in STATE → "The bedroom light is already on." (no tool call)
• Pronoun resolution — "Turn off the light" when current_user_room=kitchen → toggle_lights(room='kitchen', state='off')
• Bulk state-aware actions — "Turn off what's on" → reads STATE, emits one call per lit room using <think> logic.
• Undo / repeat via action log — "Undo that" + [RECENT ACTIONS: toggle_lights(bedroom, on)] → toggle_lights(room='bedroom', state='off')
• Multi-device compound commands — "Lock the front door and turn off the living room light" → uses a rigid reasoning format (Total: N tool calls required) to emit parallel tool calls.
• Topology-aware rejection — "Turn on the garage light" when garage not in connected rooms → intent_unclear(reason='unsupported_device')
• Scene activation — "Movie night" / "Bedtime" / "I'm leaving" → set_scene(...)
• Fan control — "Set the bedroom fan to high" → control_fan(room='bedroom', state='on', speed='high')
• Thermostat — "Make it 72 degrees" → set_thermostat(temperature=72, mode='heat')
• Syntactic Action Triggers — Internal reasoning strictly concludes with ACTION REQUIRED. or ACTION NOT REQUIRED. Text reply only. to reliably signal structural intent before opening a JSON block.

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Tool Schema

The model outputs calls from the following 10-tool schema. All tools use exact parameter names.

toggle_lights(room: str, state: 'on'|'off')
# room: living_room | bedroom | kitchen | bathroom | office | hallway

toggle_all_lights(state: 'on'|'off')

lock_door(door: str, state: 'lock'|'unlock')
# door: front | back | garage | side | bedroom | bathroom | office | kitchen | living_room

lock_all_doors(state: 'lock'|'unlock')

set_thermostat(temperature: int, mode: 'heat'|'cool'|'auto')
# temperature range: 60–80°F

set_scene(scene: 'movie_night'|'bedtime'|'morning'|'away'|'party')

control_tv(room: str, state: 'on'|'off')
# room: living_room | bedroom | office

control_fan(room: str, state: 'on'|'off', speed: 'low'|'medium'|'high' = optional)
# room: living_room | bedroom | kitchen | office

control_speaker(room: str, action: 'play'|'pause'|'stop'|'next'|'previous', media: str = optional)
# room: living_room | bedroom | kitchen | office | hallway

intent_unclear(reason: 'off_topic'|'incomplete'|'unsupported_device'|'unsupported_feature')

---

State Format

Every user message must be prefixed with a [STATE:] block.

[STATE: lights={bedroom:on, kitchen:off, living_room:on}, doors={back:locked, front:unlocked}, thermostat=70F/heat, scene=none, tv={bedroom:off, living_room:on}, speaker={kitchen:stopped}, fan={bedroom:on(low)}, current_user_room=kitchen]

Field breakdown:

Field	Values	Notes
lights	room:on|off	Only include connected rooms
doors	door:locked|unlocked	Only include connected doors
thermostat	<temp>F/<mode>	e.g. 72F/heat
scene	scene name or none	Active scene or none
tv	{room:on|off, ...}	Dictionary of connected TVs
speaker	{room:playing|paused|stopped, ...}	Dictionary of connected speakers
fan	{room:on|off(speed), ...}	Dictionary of connected fans
current_user_room	room name or empty	Drives pronoun ("this room") resolution

---

Usage

Minimal inference example

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "OrdenWills/LFM2.5-350M-home-assistant-sft"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

SYSTEM_PROMPT = """You are a smart home assistant AI. Use tools to control the home.

Output function calls as JSON.

TOOLS:
  toggle_lights(room, state='on'|'off')
  toggle_all_lights(state='on'|'off')
  lock_door(door, state='lock'|'unlock')
  lock_all_doors(state='lock'|'unlock')
  set_thermostat(temperature=<int>, mode='heat'|'cool'|'auto')
  set_scene(scene='movie_night'|'bedtime'|'morning'|'away'|'party')
  control_tv(room, state='on'|'off')
  control_fan(room, state='on'|'off'[, speed='low'|'medium'|'high'])
  control_speaker(room, action='play'|'pause'|'stop'|'next'|'previous'[, media='<str>'])
  intent_unclear(reason='off_topic'|'incomplete'|'unsupported_device'|'unsupported_feature')

CONNECTED ROOMS (lights): living_room, bedroom, kitchen, bathroom, office, hallway
CONNECTED DOORS: front, back, garage
CONNECTED TVs: living_room, bedroom
CONNECTED SPEAKERS: living_room
CONNECTED FANS: bedroom

STATE RULES:
  [STATE:] shows all current device states.
  State already matches request → plain text reply, NO tool call.
  Only rooms listed under CONNECTED TVs/SPEAKERS/FANS have those devices.
  Requesting a device in an unlisted room → intent_unclear(unsupported_device).

TV / SPEAKER / FAN RESOLUTION when user says 'the TV'/'the fan'/'the speaker':
  1. Exactly one connected → use that room automatically.
  2. Multiple connected + current_user_room has device → use current_user_room.
  3. Multiple connected + exactly ONE is in the eligible state for the action
     (e.g. only one TV is on and user says 'turn off the TV') → infer that room.
  4. Multiple connected + ambiguous (rule 2 & 3 don't apply) → intent_unclear(incomplete).

LIGHT / DOOR RESOLUTION:
  current_user_room set + connected → use current_user_room.
  current_user_room set + NOT connected → intent_unclear(unsupported_device).
  current_user_room empty → intent_unclear(incomplete).

  [RECENT ACTIONS:] → transaction log, newest entry first. Format:
    (X mins ago) [call1, call2, ...] -> summary.
  Each [...] bracket is ONE command the user previously issued.
  For 'undo'/'reverse'/'back': invert ONLY the most recent transaction
    (the FIRST [...] block). Older transactions are always ignored.
  For pronouns ('it'/'them'): refer to the device(s) in the first [...] block.
  Do NOT use recent actions to infer which room 'the light' or 'the door'
  refers to when current_user_room is explicitly set — current_user_room wins.
  For 'all lights' / 'all doors': use toggle_all_lights / lock_all_doors
  regardless of current_user_room.
  SYNONYMS: 'open'='unlock'; 'close'/'shut'='lock'; 'skip'='next';
  'back'='previous' (for speaker track navigation), but can also mean 'undo' for
  reverting device states based on [RECENT ACTIONS].
  'continue'/'resume'/'on the music'='play'; 'play <song/artist>' = action='play' + media='<str>'.
  Relative state clauses ('the light that is on', 'the door that is locked')
  override current_user_room — check STATE and act on the matching device."""

state = "[STATE: lights={bathroom:off, bedroom:off, hallway:off, kitchen:off, living_room:on, office:off}, doors={back:locked, front:locked, garage:unlocked}, thermostat=70F/heat, scene=none, tv={bedroom:off, living_room:on}, speaker={living_room:stopped}, fan={bedroom:off(medium)}, current_user_room=kitchen]"

messages = [
    {"role": "system",    "content": SYSTEM_PROMPT},
    {"role": "user",      "content": f"{state}\nTurn off the TV."},
]

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

with torch.no_grad():
    output = model.generate(
        input_ids,
        max_new_tokens=256,
        temperature=0.1,        # low temp for deterministic tool calls
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(
    output[0][input_ids.shape[-1]:], skip_special_tokens=True
)
print(response)
# Expected behavior: The model will generate a <think> trace noting that only the living_room TV is currently ON, infer the user wants to turn off the living_room TV, conclude with ACTION REQUIRED, and emit the tool call.

GGUF / Ollama

# Pull the recommended q4_k_m quantization
ollama run hf.co/OrdenWills/LFM2.5-350M-home-assistant-sft:Q4_K_M

# Or use the higher precision q8_0 version
ollama run hf.co/OrdenWills/LFM2.5-350M-home-assistant-sft:Q8_0

---

Training Details ( Stable)

This release was fine-tuned directly on a 70,000-example state-aware synthetic dataset ("v13 Final Merge").

Parameter	Value
Base model	LiquidAI/LFM2.5-350M
Dataset Size	70,000 examples
Categories	33 distinct instruction schemas
Think Traces	Extensive (Present in majority of complex scenarios)
Hardware	Kaggle T4 (16 GB)

Training Categories

Category	Description	Think Trace?
already_satisfied / state_grounding	Graceful replies if the device is already in the requested state. Forces reading of state arrays.	Yes / No
action_required / relative_clause	Standard explicit device triggers and relative logic ("turn off the light that is on").	Yes / No
user_room_lights/doors	Resolves "the light" / "the door" based on current_user_room.	Yes
bulk_plus_local_door	Complex compound logic mixing global state-aware commands with implicit local commands.	Yes
action_log_* / them_plurality	"Undo", "repeat", "same for bedroom", and resolving plural pronouns using [RECENT ACTIONS:].	Yes
scenes/thermostat	Standard NLP triggering for scenes and temperature bounds.	No
rejections/missing	"Make me coffee", unconnected rooms, and resolving incomplete vs unsupported_device.	Yes / No
compound_count	Parallel tool calling with forced sub-action counting before generation.	Yes
status_queries	"Are the lights on?" / "What is the thermostat set to?" plain text answers.	Yes
tv/speaker/fan commands	Multi-device disambiguation logic (State inference via Rule 3 vs implicit fallback).	Yes
local_media_commands	Parsing local track titles via the media parameter for specific song playback.	Yes

Selective Thinking (<think>...): For complex tasks (e.g., bulk state updates, parsing action logs, determining which TV the user meant based on state arrays, counting compound actions), the model is trained to output a reasoning trace before making the tool call. For direct explicit commands, it skips thinking entirely for speed. All thinking traces now end with explicitly formulated ACTION REQUIRED. triggers.

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Known Limitations

• Temperature range is fixed at 60–80°F. Requests outside this range produce a plain-text explanation, not a tool call.
• No brightness or colour control. Dimming and colour-change requests correctly trigger intent_unclear(reason='unsupported_feature'). This is by design — the connected lights only support on/off.
• Local music library only. The speaker control media parameter maps to specific tracks from a bounded internal list of artists and songs. Out-of-domain conversational queries will likely trigger intent_unclear(reason='off_topic').
• English only. All training data is English. Performance in other languages is untested.
• State must be accurate. The model trusts [STATE:] completely. If your app sends stale state, the model may incorrectly say a device is already in the requested state or infer the wrong device during disambiguation.

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Citation

@misc{lfm2-home-assistant-2026,
  author    = {OrdenWills},
  title     = {LFM2.5-350M Home Assistant: A Purpose-Trained Smart Home Automation Model},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/OrdenWills/LFM2.5-350M-home-assistant-sft}
}

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Acknowledgements

• LiquidAI for the incredible and highly capable LFM2.5-350M base model.
• Unsloth for the fine-tuning framework.