Lura 1.1
Lura 1.1 is an ultra-lightweight language model developed by SEN-AGI. It is the successor to Lura 1.0, fine-tuned from the Qwen 2.5 500M (0.5B) base model.
Lura 1.1 demonstrates that carefully curated, high-quality synthetic data allows compact models to punch significantly above their weight class.
🚀 What's New in Lura 1.1?
Compared to Lura 1.0, Lura 1.1 brings noticeable improvements across key everyday tasks:
- Better Instruction Following: Adheres more reliably to formatting, constraints, and multi-turn instructions.
- Improved Elementary Math & Logic: Better step-by-step handling of simple arithmetic and logical puzzles.
- Basic Code Generation: Capable of writing small scripts, basic functions, and code explanations.
- High Efficiency: Runs smoothly on CPUs, edge devices, smartphones, and low-VRAM setups.
🛠️ Training & Dataset
- Base Model:
Qwen/Qwen2.5-0.5B - Data Strategy: Rather than using generic, multi-gigabyte open datasets, Lura 1.1 was fine-tuned on 4,000–5,000 custom synthetic samples. These samples were generated and filtered using frontier (SOTA) large language models to maximize density, diversity, and reasoning quality.
- Zero-Cost Engineering: Trained entirely on Google Colab's free T4 GPU tier and managed directly from a single mobile device without requiring dedicated local GPUs or desktop computers.
💻 How to Use
You can easily run Lura 1.1 using Hugging Face's transformers library:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "SEN-AGI/Lura-1.1-500m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
prompt = "Explain why the sky is blue in three simple sentences."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.7,
top_p=0.9
)
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(response)
⚠️ Limitations & Biases
Because Lura 1.1 is built on a 500M parameter architecture, it has natural
capacity limits:
- Hallucinations: It may state incorrect information confidently, especially
on niche or complex factual queries.
- Complex Reasoning: It is not intended for advanced mathematics, competitive
programming, or deep multi-hop logic.
- Recommended Use Cases: Everyday basic assistance, simple
formatting/summarization, lightweight offline experimentation, and edge
device deployment.
👨💻 Developed By
- Creator: SEN-AGI
- Focus: Pushing the limits of compact language models through high-quality
synthetic data curation.