Jabr7
/

Mini-Boni

@@ -2,199 +2,74 @@
 library_name: transformers
 tags:
 - unsloth
 ---
-# Model Card for Model ID
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 ## Model Details
 ### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 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 = "YOUR_HF_USERNAME/YOUR_ADAPTER_REPO"
+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))