How it was made
- Base: AuroraGPT-700M raw pretrained base (Llama-style: hidden 1536, 27 layers, 12 heads / 2 KV, 32k vocab, ctx 2048).
- Behavior distillation: ~9.4M tokens of full-parameter SFT on the base, distilling chat behavior from Qwen3-4B-Instruct (persona/voice imprinted at the source), mixed with hand-authored data for identity, instruction-following, tool-calling (web_search / calculator / fetch_url), false-premise correction, and gibberish handling. 2 epochs, sequence-packed.
What it's good at
- 💬 Chat feel — warm, natural, conversational (distilled from a 4B teacher)
- 🧭 Instruction-following — one-word / count / format compliance
- 🪪 Consistent identity — knows it's AuroraGPT by UltraLabs
- 🛠️ Tool-calling — emits valid
<tool_call> and reads back <tool_response> (this is the whole point)
- 🧠 Context/name recall across turns
Honest limitations
- Raw knowledge is limited — it's 700M. Use tools for facts. Closed-book, it will not know long-tail trivia.
- Math/reasoning is fragile — it shows working but fumbles multi-digit arithmetic. Use the calculator tool.
- Occasional false-premise agreement and 700M-scale coherence wobble on hard prompts.
<|system|>{system}<|end|><|user|>{user}<|end|><|assistant|>{reply}<|end|>
Tool call (model emits): <tool_call>\n{"name": "...", "arguments": {...}}\n</tool_call>
Tool result (feed back as a user turn): <|user|><tool_response>\n{result}\n</tool_response><|end|>
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("SmallAICreator/AuroraGPT-Qwen-Distill")
model = AutoModelForCausalLM.from_pretrained("SmallAICreator/AuroraGPT-Qwen-Distill")
msgs = [{"role": "user", "content": "yo"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
print(tok.decode(model.generate(ids, max_new_tokens=64)[0][ids.shape[1]:], skip_special_tokens=True))
On-device (llama.cpp / GGUF)
A ready-to-run Q8_0 GGUF (AuroraGPT-Distill.Flagship.Q8_0.gguf, 753MB) is included, with a tool-capable chat template embedded so mobile GGUF apps show the tool picker.
Made by UltraLabs. EOS token is <|end|>.