|
|
--- |
|
|
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit |
|
|
tags: |
|
|
- text-generation-inference |
|
|
- transformers |
|
|
- unsloth |
|
|
- gemma3 |
|
|
license: apache-2.0 |
|
|
language: |
|
|
- en |
|
|
--- |
|
|
|
|
|
# 🧠 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`](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit), optimized and accelerated with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL for instruction-based text generation tasks. |
|
|
|
|
|
--- |
|
|
|
|
|
## 🔍 Model Summary |
|
|
|
|
|
- **Base Model:** `unsloth/gemma-3-4b-it-unsloth-bnb-4bit` |
|
|
- **Fine-Tuned By:** [Webkul](https://webkul.com) |
|
|
- **License:** Apache-2.0 |
|
|
- **Language:** English |
|
|
- **Model Type:** Instruction-tuned (4-bit quantized) |
|
|
- **Training Boost:** ~2x faster training with Unsloth optimizations |
|
|
|
|
|
--- |
|
|
|
|
|
## 📚 Fine-Tuning Dataset |
|
|
|
|
|
This model has been fine-tuned specifically on official UnoPIM documentation and user guides available at: |
|
|
|
|
|
👉 **[https://docs.unopim.com/](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 |
|
|
|
|
|
--- |
|
|
|
|
|
## 💡 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** |
|
|
|
|
|
--- |
|
|
|
|
|
## 🚀 Quick Start |
|
|
|
|
|
You can run this model with Hugging Face’s `transformers` library: |
|
|
|
|
|
```python |
|
|
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. |
