Jabr7/Mini-Boni

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

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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.

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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.

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How to Get Started with the Model

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))