metadata
license: apache-2.0
base_model: LiquidAI/LFM2.5-1.2B-Instruct
tags:
- homie
- personal-assistant
- fine-tuned
- local-first
- privacy
- tool-use
pipeline_tag: text-generation
language:
- en
Homie -- Personal AI Assistant
Homie is a privacy-first, local AI assistant with identity baked into the weights. No system prompt needed. The model inherently knows it's Homie.
Features
- Identity: Knows it's Homie, not a generic chatbot
- Tool-aware: Understands email, calendar, files, git, web search, voice, mesh
- Privacy-first: Emphasizes local-only, no cloud
- Concise: Leads with answers, code-first for dev questions
- Secure: Refuses prompt injection, never leaks credentials
- Multi-device: Understands mesh networking and device sync
- Learning: Remembers preferences across sessions
Training
- Base model: LiquidAI/LFM2.5-1.2B-Instruct (Apache 2.0)
- Method: QLoRA (rank 64, alpha 128, 0.54% trainable)
- Data: 69 hand-crafted examples across 10 categories (x3 = 207 total)
- DPO: 10 preference pairs (Homie-style vs generic)
- Categories: identity, tools, mesh, memory, security, coding, proactive, errors, flow, system
- Epochs: 5, Loss: 1.414, Accuracy: 92%
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Muthu88/Homie", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Muthu88/Homie", trust_remote_code=True)
messages = [{"role": "user", "content": "Who are you?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
Part of the Homie AI Project
- GitHub: https://github.com/MSG-88/Homie
- Website: https://heyhomie.app