README.md

metadata

language:
  - en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
  - llama
  - causal-lm
  - code-generation
  - lightweight
  - 1.54B
base_model:
  - Qwen/Qwen2.5-Coder-1.5B-Instruct

HOS-OSS-1.54B

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

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))