---
title: Robe Iniesta Persona Chat
emoji: 🎸
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 5.12.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Spanish persona chat — Qwen 7B + LoRA, under 32B
tags:
  - build-small-hackathon
  - zerogpu
  - spanish
  - persona
  - lora
  - qwen2
models:
  - Qwen/Qwen2.5-7B-Instruct
  - kabesaml/robe-iniesta-lora
---

# Robe Iniesta — Persona Chat

Fine-tuned **Qwen2.5-7B-Instruct** (7B parameters) with a LoRA adapter trained on public interview transcripts. SFT + DPO pipeline. Well under the **32B parameter cap**.

⚠️ **Fan project.** This is not Robe Iniesta. It may hallucinate biographical facts.

**Live Space:** https://huggingface.co/spaces/build-small-hackathon/robe-iniesta

## Hackathon submission assets

| Requirement | Status | Link |
|---|---|---|
| Model ≤32B | ✅ Qwen2.5-7B (7B) | [base model](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
| Gradio app in Build Small org | ✅ | This Space |
| Demo video | ⬜ TODO | **REPLACE:** https://youtu.be/YOUR_DEMO_ID |
| Social media post | ⬜ TODO | **REPLACE:** https://x.com/kabesaml/status/YOUR_POST_ID |
| ZeroGPU | ✅ | `@spaces.GPU(duration=120)` in `app.py` |

### Demo video

**Demo video:** https://youtu.be/YOUR_DEMO_ID

1–3 min walkthrough showing:
1. Opening the Space and waiting for the first GPU response (cold start)
2. Asking "¿Qué significa Extremoduro para ti?"
3. A follow-up question to show multi-turn chat
4. Brief note that it's a style simulation, not the real person

Upload to YouTube (unlisted is fine) or attach video to an X post, then replace the URL above.

### Social media post

**Social post:** https://x.com/kabesaml/status/YOUR_POST_ID

Example post text:

```
Built a Spanish persona chatbot for the @huggingface Build Small hackathon 🎸

Qwen2.5-7B (7B) + LoRA, SFT → DPO on public interview transcripts.
Runs on ZeroGPU. Under the 32B cap.

Try it: https://huggingface.co/spaces/build-small-hackathon/robe-iniesta

#BuildSmall #HuggingFace #Qwen #LoRA
```

Post on X or LinkedIn, then paste the post URL above.

## Model & training

| Component | Detail |
|---|---|
| Base model | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) — **7B params** |
| Adapter | [kabesaml/robe-iniesta-lora](https://huggingface.co/kabesaml/robe-iniesta-lora) |
| Training | LoRA SFT → DPO preference tuning on Modal A100 |
| Quantization | 4-bit NF4 at inference (fits ZeroGPU) |
| Source repo | https://github.com/kabesaml/robellm *(update if public)* |

## Space hardware

Set in Space **Settings → Hardware → ZeroGPU**.

The chat handler uses `@spaces.GPU(duration=120)`. First request after idle may take ~30–60s (model load).

## Local development

From the robellm repo:

```bash
uv sync --extra dev
uv pip install -e ".[train]" spaces
uv run python space/app.py
```