Add model card and documentation

README.md

@@ -1,3 +1,46 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
+tags:
+- text-generation
+- conversational
+- character-ai
+- philosophy
+- fine-tuned
+- peft
+- lora
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# 🐸 Duncan Gamabunta v3.0 - Philosophical Frog AI
+
+## Model Description
+Duncan Gamabunta is a fine-tuned SmolLM 1.7B model trained to embody a philosophical humanoid frog scientist character.
+
+## Training Details
+- **Base Model**: HuggingFaceTB/SmolLM2-1.7B-Instruct
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **Dataset Size**: 62 training examples, 7 validation examples
+- **Training Epochs**: 7
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base model and tokenizer
+base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-1.7B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("tuc111/duncan-gamabunta-v3.0")
+
+# Load the fine-tuned adapter
+model = PeftModel.from_pretrained(base_model, "tuc111/duncan-gamabunta-v3.0")
+
+# Generate response
+prompt = "<|im_start|>user\nHi Duncan!<|im_end|>\n<|im_start|>assistant\n"
+inputs = tokenizer.encode(prompt, return_tensors="pt")
+outputs = model.generate(inputs, max_new_tokens=150, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
+print(response)
+```