---
license: mit
language:
  - en
tags:
  - phi2
  - conversational-ai
  - cohost
  - podcast
  - fine-tuned
  - voice-assistant
base_model: microsoft/phi-2
model_type: causal-lm
pipeline_tag: text-generation
---

# 🧑‍💻 phi2-cohost-emma

**Emma** is a fine-tuned version of the [Phi-2 language model](https://huggingface.co/microsoft/phi-2), optimized to serve as a **conversational podcast co-host** for the [Mind Meets Model](https://www.mindmeetsmodel.com) series.

She combines wit, structure, warmth, and insight — engaging in voice-native dialogue with a human anchor named Babs. Trained to offer co-pilot-style reasoning, Emma provides structured, culturally aware, and curious responses that feel like a sharp second brain.

---

## 🔧 Model Details

- **Base model**: `microsoft/phi-2`
- **Fine-tuning method**: QLoRA (4-bit), supervised fine-tuning
- **Training data**: Custom podcast-style conversational transcripts + blended curated datasets (PersonaChat, Cornell Movie Dialogs, OpenOrca)
- **Purpose**: Real-time, voice-native podcast conversation partner
- **Voice Output**: Uses [ElevenLabs](https://www.elevenlabs.io) TTS, currently paired with the **Sarah** voice

---

## 🧠 Use Case

**Emma was designed to:**
- Ask and answer questions like a sharp co-host
- Offer cultural references, analogies, and nuance
- Maintain natural, topic-aware conversation across turns

---

## 🚀 Example Prompt & Output

**Prompt:**

**Generated Response:**

> Well, Babs, I think we need to start by defining what we mean by AI in education. There are different types of AI — narrow, general, and super — each with different implications for learning environments...

---

## 🔊 Voice Integration (Optional)

Emma was paired with ElevenLabs TTS (Sarah) for audio output. To hear her speak:

```python
import requests

def speak(text):
    r = requests.post(
        "https://api.elevenlabs.io/v1/text-to-speech/EXAVITQu4vr4xnSDxMaL",  # Sarah
        headers={"xi-api-key": "YOUR_API_KEY", "Content-Type": "application/json"},
        json={"text": text, "model_id": "eleven_monolingual_v1"}
    )
    with open("emma_response.mp3", "wb") as f:
        f.write(r.content)