HOS-OSS-1.54B is a lightweight 1.54B parameter causal language model optimized for text and code generation tasks.
It is designed for fast inference, low resource usage, and local deployment.

🚀 Overview

Model size: ~1.54B parameters
Architecture: LLaMA-style decoder-only transformer
Base model: Qwen2.5-Coder-1.5B-Instruct (distilled / adapted)
Framework: 🤗 Transformers
Use cases:
- Code generation
- Instruction following
- Chat-style completion
- Lightweight local AI assistant

⚡ Features

Fast inference on low-end GPUs
Runs on Kaggle / Colab without large VRAM
Suitable for edge deployment
Clean instruction-response formatting

🧠 Example Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "hydffgg/HOS-OSS-1.54B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "User: Write a Python Hello World\nAssistant:"

inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))