QWEN2.5B

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct trained on the Alpaca Dataset using Low-Rank Adaptation (LoRA) and Supervised Fine-Tuning (SFTTrainer).

Model Details

• Developed by: Taha A Siddiqui
• Model Type: Causal Language Model (Instruction-tuned)
• Language(s): English
• License: Apache-2.0
• Finetuned from model: Qwen/Qwen2.5-0.5B-Instruct

Intended Uses & Limitations

Direct Use

This model is intended for general instruction-following, basic reasoning, text generation, and conversational Q&A.

Limitations

• Inherits knowledge cutoffs and biases from the underlying base model.
• Being a lightweight model (0.5B parameters), it may hallucinate complex facts or logic tasks.

How to Get Started with the Model

Use the code below to load and run inference with this model:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen2.5-0.5B-Instruct"
peft_model_id = "tahasiddiqui7/Qwen2.5B"

tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load fine-tuned LoRA adapter onto base model
model = PeftModel.from_pretrained(base_model, peft_model_id)

prompt = "### Instruction:\nExplain the concept of fine-tuning in machine learning.\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))