---
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)
```