Update README.md

README.md

@@ -1,3 +1,148 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- pt
+- en
+- fr
+- es
+base_model:
+- PleIAs/Baguettotron
+pipeline_tag: text-generation
+library_name: peft
+---
+
+# DogeAI-v2.0 🐶🔥 (LoRA Weights Only)
+
+⚠️ **Important Notice**  
+This repository **does NOT contain a full language model**.
+
+It only provides **LoRA fine-tuned weights** for the base model **Baguettotron**.  
+To use DogeAI-v2.0, you **must load it on top of the base model**.
+
+---
+
+## 🔍 What is this?
+
+DogeAI-v2.0 is a **LoRA adaptation** trained to give the base model:
+
+- Better conversational flow
+- Clearer reasoning
+- Stronger math and logic responses
+- A more direct and confident assistant style
+
+This repository contains **only the LoRA weights**, which are lightweight and efficient.
+
+---
+
+## 🧠 Base Model (Required)
+
+You must use the following base model:
+
+PleIAs/Baguettotron
+
+yaml
+Copiar código
+
+Without it, these weights **will not work**.
+
+---
+
+## 🧩 What is LoRA?
+
+LoRA (Low-Rank Adaptation) is a fine-tuning technique that:
+- Keeps the original model frozen
+- Applies small, efficient weight updates
+- Uses much less memory than full fine-tuning
+
+This makes DogeAI-v2.0:
+- Fast to load
+- Easy to experiment with
+- Friendly for consumer hardware
+
+---
+
+## 🚀 How to Use
+
+### 1️⃣ Install dependencies
+
+```bash
+pip install torch transformers peft
+2️⃣ Load the model + LoRA
+python
+Copiar código
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+BASE_MODEL = "PleIAs/Baguettotron"
+LORA_PATH = "dogeai_v2_lora"  # or dogeai_v2_lora_10pct
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+tokenizer.pad_token = tokenizer.eos_token
+
+print("Loading base model...")
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float32
+)
+
+print("Applying DogeAI-v2.0 LoRA 🐶🔥")
+model = PeftModel.from_pretrained(model, LORA_PATH)
+model.eval()
+3️⃣ Chat loop example
+python
+Copiar código
+print("\nDogeAI-v2.0 ready! Type 'exit' to quit.\n")
+
+while True:
+    user_input = input("You: ")
+    if user_input.lower() in ["exit", "quit"]:
+        break
+
+    prompt = f"""
+<|im_start|>user
+{user_input}
+<|im_end|>
+<|im_start|>assistant
+"""
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+    inputs.pop("token_type_ids", None)
+
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.95,
+            repetition_penalty=1.2,
+            eos_token_id=tokenizer.eos_token_id
+        )
+
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    response = response.split("<|im_start|>assistant")[-1].strip()
+
+    print(f"\nDogeAI 🐶: {response}\n")
+💻 Hardware Notes
+Runs on CPU (slow but works)
+
+Recommended: GPU for better speed
+
+LoRA keeps memory usage low compared to full fine-tuning
+
+🎯 What this is NOT
+❌ Not a standalone model
+
+❌ Not a GGUF / quantized release
+
+❌ Not an instruction-following base model by itself
+
+This is an enhancement, not a replacement.
+
+🐕 DogeAI Philosophy
+Fast. Honest. No hallucinated confidence.
+Clear answers, real reasoning, no nonsense.
+
+Made for experimentation, learning, and pushing models further 🚀