README.md

---
library_name: transformers
tags:
- unsloth
- lora
- gemma
- whatsapp
license: apache-2.0
language:
- es
base_model:
- unsloth/gemma-2-2b-it-bnb-4bit
---

# Model Card for Whatsapp-Finetuned LoRA (Gemma-2-2B-IT-4bit)

## Model Details

### Model Description
This is a LoRA adapter trained on personal WhatsApp conversations, applied on top of **`unsloth/gemma-2-2b-it-bnb-4bit`**, an instruction-tuned Gemma 2B model in 4-bit quantization.  
The adapter specializes the base model toward informal Spanish conversational style, slang, and context typical of WhatsApp chats.  

- **Developed by:** Private (Boni)  
- **Model type:** LoRA adapter for causal language modeling  
- **Language(s):** Spanish (es)  
- **Finetuned from model:** `unsloth/gemma-2-2b-it-bnb-4bit`  

### Model Sources
- **Base model:** [unsloth/gemma-2-2b-it-bnb-4bit](https://huggingface.co/unsloth/gemma-2-2b-it-bnb-4bit)  

---

## Uses

### Direct Use
- Chatbots and assistants that mimic WhatsApp-style Spanish conversations.  
- Experimentation with low-rank adapters on personal datasets.  

### Downstream Use
- Can be merged with the base model for full fine-tuned inference.  
- Can be combined with other adapters for multi-domain behavior.  

### Out-of-Scope Use
- Production deployment without careful filtering (the dataset is personal, informal, and may not generalize).  
- Sensitive domains like healthcare, law, or safety-critical applications.  

---

## Bias, Risks, and Limitations
- The dataset consists of personal WhatsApp conversations, which may include biases, informal expressions, and idiosyncratic slang.  
- The model may reflect private communication style and does not guarantee factual correctness.  
- Limited training size means performance outside the conversational domain is reduced.  

### Recommendations
Users should treat outputs as experimental. Avoid relying on this model for factual or professional contexts.  

---

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "unsloth/gemma-2-2b-it-bnb-4bit"
adapter = "Jabr7/Mini-Boni"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

prompt = "Hola, ¿cómo estás?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))