README.md

---
title: Vineyard Plotting-Code Models
emoji: 🍇
colorFrom: purple
colorTo: green
sdk: gradio
sdk_version: 6.22.0
python_version: '3.12'
app_file: app.py
pinned: false
license: other
short_description: Six LoRA adapters that plot vineyard data
---

# 🍇 Vineyard plotting-code models

Six LoRA adapters fine-tuned to write matplotlib/seaborn code against synthetic
vineyard DataFrames. Pick an adapter from the dropdown, pick one of the 12
DataFrames, and ask for a chart.

**The generated code is then executed** in a short-lived subprocess and the chart
shown is the one it actually produced — the same execution check the offline
evaluation scores, so nothing here can pass off plausible-looking code that does
not run.

## What's in the dropdown

| Model | Base | Adapter |
|---|---|---|
| Qwen2.5-Coder-0.5B · bf16 | `Qwen/Qwen2.5-Coder-0.5B-Instruct` | `models/qwen2.5-coder-0.5b-plotter-lora` |
| Qwen2.5-Coder-1.5B · bf16 (best checkpoint) | `unsloth/Qwen2.5-Coder-1.5B-Instruct` | `models/qwen2.5-coder-1.5b-plotter-lora-bf16-best` |
| Qwen2.5-Coder-1.5B · bf16 (final step) | `unsloth/Qwen2.5-Coder-1.5B-Instruct` | `models/qwen2.5-coder-1.5b-plotter-lora-bf16` |
| Qwen2.5-Coder-1.5B · 4-bit NF4 run | `Qwen/Qwen2.5-Coder-1.5B-Instruct` | `models/qwen2.5-coder-1.5b-plotter-lora` |
| Phi-3.5-mini-instruct · 3.8B | `microsoft/Phi-3.5-mini-instruct` | `models/phi35-mini-instruct-lora` |
| LFM2-2.6B | `LiquidAI/LFM2-2.6B` | `models/lfm-2.6b-lora` |

The **"Use the fine-tuned adapter"** checkbox turns the adapter off, so you can
run the same prompt against the untuned base and see what the fine-tuning bought.
On the 0.5B, the honest answer is house style rather than reliability: the adapter
takes `tight_layout()` from 1/10 to 10/10 and drops invented columns to 0/10,
without improving execution pass rate.

## How the prompt is built

Identical to training, or the model is off-distribution. A fixed system turn,
then a user turn holding the DataFrame preview (`schemas.df_preview` — the single
source of truth for that string) followed by the request.

## Notes

- The first request for a given model downloads its base weights; later ones are
  cached. Only one model is held in memory at a time.
- Generated code runs with restricted builtins — no `os`, `open`, `eval`,
  `compile`, `subprocess` — and only data/plotting imports. That is a
  proportionate guard against hallucinated code, not a hardened sandbox.
- Base model licences differ: Qwen2.5-Coder is Apache-2.0, Phi-3.5-mini is MIT,
  LFM2 is under the LFM Open License. The adapters are derivative of their
  respective bases.