qwen3-8b-finetuned-LoRA

🧠 Qwen3-8B-Finetuned-LoRA (Merged Model)

This is a merged version of the Qwen3-8B model, fine-tuned using LoRA to enhance reasoning, instruction-following, and domain-specific performance.

The LoRA adapter has been merged into the base model, making it fully compatible with the Hugging Face Inference API and local inference.

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🚀 Quick Start – Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/varunpruthviraj/qwen3-8b-finetuned-LoRA"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def ask_qwen(prompt):
    response = requests.post(API_URL, headers=headers, json={
        "inputs": prompt,
        "parameters": {"max_new_tokens": 256, "temperature": 0.7}
    })
    result = response.json()
    if isinstance(result, list):
        return result[0]["generated_text"]
    return result.get("generated_text", result)

# Example
answer = ask_qwen("Explain quantum computing in simple terms.")
print(answer)

Inference Endpoint

YouYou can deploy this model to a Hugging Face Inference Endpoint for a production-ready API.

Usage (with transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "varunpruthviraj/qwen3-8b-finetuned-LoRA"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

prompt = "Explain the concept of knowledge distillation in simple terms."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))