rogersam/tinyllama-instruct-lite-v1

TinyLlama Instruct Lite v1

📌 Model Summary

rogersam/tinyllama-instruct-lite-v1 is a LoRA fine-tuned TinyLlama model using Unsloth. It is designed for instruction-following tasks in English + Tamil, such as:

• —General Q&A
• —Summarization
• —Basic math & reasoning
• —English ↔ Tamil translation

This project demonstrates how a lightweight 1B model can be adapted for multiple domains with limited resources.

🔎 Model Details

• —Developed by: Roger Samuel J (Hugging Face Profile)
• —Model type: Causal LM (decoder-only)
• —Languages: English, Tamil
• —License: Same as base model (TinyLlama)
• —Fine-tuned from: unsloth/tinyllama-bnb-4bit
• —Method: LoRA via PEFT + Unsloth

📂 Model Sources

• —Model Repo: rogersam/tinyllama-instruct-lite-v1
• —Base Model: unsloth/tinyllama-bnb-4bit

💡 Uses

Direct Use

• —Running lightweight instruction tasks on CPU/GPU
• —Translating English ↔ Tamil sentences
• —Answering short questions and reasoning queries
• —Summarizing small texts

Out-of-Scope

• —Sensitive decision-making (finance, healthcare, law)
• —Long context generation (>512 tokens)
• —Production-grade chatbots

⚠️ Bias, Risks & Limitations

• —Small dataset → may hallucinate facts
• —Not aligned for safety or toxicity filtering
• —Limited Tamil coverage (basic sentences only)

Recommendation: Use for demo & educational purposes only.

🚀 How to Get Started

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "rogersam/tinyllama-instruct-lite-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Translate English to Tamil: How are you?"
print(pipe(prompt, max_new_tokens=50)[0]["generated_text"])