| --- |
| base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit |
| tags: |
| - text-generation-inference |
| - transformers |
| - unsloth |
| - gemma3 |
| license: apache-2.0 |
| language: |
| - en |
| --- |
| |
| # 🚀 Fine-tuned Gemma 3 Model (4B, 4-bit) by Webkul |
|
|
| This repository contains a fine-tuned version of [Unsloth's](https://github.com/unslothai/unsloth) `gemma-3-4b-it` model, optimized for lightweight 4-bit inference and instruction tuning using Hugging Face's [TRL](https://github.com/huggingface/trl) and Unsloth's speed-optimized framework. |
|
|
| --- |
|
|
| ### What is UnoPim |
|
|
| [UnoPim](https://unopim.com/) 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 Details |
|
|
| - **Base Model:** [`unsloth/gemma-3-4b-it-unsloth-bnb-4bit`](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit) |
| - **Fine-tuned By:** [Webkul](https://webkul.com) |
| - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
| - **Language:** English (`en`) |
| - **Model Size:** 4B parameters (4-bit quantized) |
| - **Frameworks Used:** Unsloth, Hugging Face Transformers, TRL |
|
|
| --- |
|
|
| ## 📚 Fine-tuning Dataset |
|
|
| This model was fine-tuned on unopim dev documentation available at [https://devdocs.unopim.com/](https://devdocs.unopim.com/), focusing on structured software documentation and developer support content. |
|
|
| --- |
|
|
| ## 💡 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 |
|
|
| --- |
|
|
| ## 🧪 How to Use |
|
|
| You can use this model with the Hugging Face `transformers` library: |
|
|
| ```python |
| 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. |
|
|
| --- |
|
|
|
|
