webkul/unopim-devdocs

🚀 Fine-tuned Gemma 3 Model (4B, 4-bit) by Webkul

This repository contains a fine-tuned version of Unsloth's gemma-3-4b-it model, optimized for lightweight 4-bit inference and instruction tuning using Hugging Face's TRL and Unsloth's speed-optimized framework.

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🔧 Model Details

• Base Model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
• Fine-tuned By: Webkul
• License: Apache 2.0
• Language: English (en)
• Model Size: 4B parameters (4-bit quantized)
• Frameworks Used: Unsloth, Hugging Face Transformers, TRL

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📚 Fine-tuning Dataset

This model was fine-tuned on unopim dev documentation available at https://devdocs.unopim.com/, focusing on structured software documentation and developer support content.

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💡 Intended Use

• Conversational AI assistants trained on UnoPIM developer docs
• API documentation question answering
• Developer tools and chatbot integrations
• Contextual helpdesk or onboarding bots for UnoPIM products

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🧪 How to Use

You can use this model with the Hugging Face transformers library:

from transformers import AutoTokenizer, AutoModelForCausalLM

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

input_text = "How do I integrate the UnoPIM API for product syncing?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📄 License This model is licensed under the Apache License 2.0.

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