---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
license: llama3.1
pipeline_tag: text-generation
tags:
- lora
- peft
- text-to-sql
- tinyforge
- low-vram
---

# tinyforge: Llama-3.1-8B-Instruct LoRA for text-to-SQL, trained on a 4 GB GPU

A 14 MB LoRA adapter (r=16) trained with [tinyforge](https://github.com/rajratangm/tinyforge) on a **4 GB RTX 3050 Ti laptop
GPU** using layer streaming: 200 steps in 9.5 minutes. Training data: 1,800 question/SQL pairs over six public seaborn
tables (titanic, mpg, tips, car_crashes, planets, healthexp), each answer verified by executing the SQL.
Use under the Llama 3.1 Community License.

## Results (read the caveats)

100 questions about a table the model never saw (penguins), served with llama.cpp (adapter f16 on a Q4_K_M base), greedy decoding.

| Model | Execution accuracy |
|---|---|
| This adapter | **100%** |
| Base told to "reply with exactly one SQLite query" | 93% |

Caveats, honestly:
- 100% is a ceiling: the instructed base is already at 93%, so this test cannot show how much headroom tuning adds.
  All 7 base misses are one malformed `MAX`/`MIN` call, so the gain is **consistency, not new capability**.
- Questions use the same 12 templates as training, on a new schema: it measures transfer to new tables, not new kinds of questions.
- On harder question shapes the same 8B model scored 98% vs 97% (no meaningful gain over a well-prompted base).
- n=100, one run, no confidence interval. Raw numbers: `eval_results.json`.

## Use

```python
from transformers import AutoModelForCausalLM
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct", device_map="auto")
model = PeftModel.from_pretrained(base, "RajGMore/tinyforge-llama3.1-8b-sql-lora")
```