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πŸš€ LamoFast-Tiny-v1 (0.5B)

LamoFast(Language model For astronmy)-Tiny is a lightweight, high-performance generative language model based on the Qwen2.5-0.5B architecture. It has been fine-tuned to act as a specialized assistant for Astronomy and Space Science, while maintaining impressive general conversational capabilities in both Hebrew and English.

✨ Key Highlights

  • Ultra-Lightweight: At only 500 million parameters, it runs lightning-fast on CPUs, mobile devices, and low-end GPUs.
  • Bilingual Mastery: Seamlessly handles queries in English and Hebrew.
  • Domain Expert: Fine-tuned on a curated astronomy dataset for higher accuracy in space-related topics.
  • Quantization Friendly: Optimized for GGUF conversion, making it perfect for local LLM tools like LM Studio and Ollama.

πŸ“Š Technical Specifications

  • Base Model: Qwen/Qwen2.5-0.5B
  • Parameters: 494M
  • Training Method: Full Fine-Tuning
  • Precision: bfloat16
  • Context Window: 512 tokens (Optimized for concise, fast responses)

πŸš€ Quick Start (Python)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Raziel1234/LamoFast-1.0"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

prompt = "<|user|>\nExplain the Big Bang theory in simple terms.<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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