unopim-docs-gemma-finetuned

🧠 Gemma 3 (4B) Fine-Tuned on UnoPIM Docs — by Webkul

This is a fine-tuned version of unsloth/gemma-3-4b-it-unsloth-bnb-4bit, optimized and accelerated with Unsloth and Hugging Face's TRL for instruction-based text generation tasks.

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What is UnoPim

UnoPim is an open-source Product Information Management (PIM) system built on the Laravel framework. It helps businesses organize, manage, and enrich their product information in one central repository.

🔍 Model Summary

• Base Model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
• Fine-Tuned By: Webkul
• License: Apache-2.0
• Language: English
• Model Type: Instruction-tuned (4-bit quantized)
• Training Boost: ~2x faster training with Unsloth optimizations

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📚 Fine-Tuning Dataset

This model has been fine-tuned specifically on official UnoPIM documentation and user guides available at:

👉 https://docs.unopim.com/

Content Covered:

• Product Information Management (PIM) workflows
• Admin dashboard and module configurations
• API usage and endpoints
• User roles and access control
• Product import/export and sync logic
• Custom field and attribute setups
• Troubleshooting and common use cases

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💡 Use Cases

This model is designed for:

• 🧾 Q&A on UnoPIM documentation
• 💬 Chatbots for UnoPIM technical support
• 🧠 Contextual assistants inside dev tools
• 🛠️ Knowledge base automation for onboarding users

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🚀 Quick Start

You can run this model with Hugging Face’s transformers library:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "webkul/gemma-3-4b-it-unopim-docs"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "How can I import products in bulk using UnoPIM?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📄 License This model is distributed under the Apache 2.0 License. See LICENSE for more information.