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license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
tags:
- code
- python
- synthetic-data
- numpy
- pandas
- vectorized
library_name: peft
pipeline_tag: text-generation
---
# FastData-LM-3B-SFT
**FastData-LM-3B-SFT** is a specialized language model fine-tuned on the [`thlurte/VSG-lite-1.5k`](https://huggingface.co/datasets/thlurte/VSG-lite-1.5k) dataset. It functions as a zero-shot **High-Throughput Vectorized Data Compiler** — translating dataset schema requirements into **100% vectorized, loop-free** NumPy and Pandas execution graphs.
## Model Details
- **Base Architecture**: [`Qwen/Qwen2.5-Coder-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct)
- **Fine-Tuning Dataset**: [`thlurte/VSG-lite-1.5k`](https://huggingface.co/datasets/thlurte/VSG-lite-1.5k)
- **Primary Domain**: Vectorized Synthetic Data Generation across 13 industrial schemas.
- **Strict Anti-Loop Constraint**: Eliminates `.apply()`, `.iterrows()`, and all `for`/`while` row-level iteration.
## Usage Example
```python
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "thlurte/FastData-LM-3B-SFT",
max_seq_length = 2048,
load_in_4bit = True,
)
messages = [
{"role": "system", "content": "You are a zero-shot Python Data Compiler. Generate 100% vectorized NumPy/Pandas code."},
{"role": "user", "content": "Generate a synthetic bank ledger dataset with entry_id, balance, and transaction_type."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
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