FastData-LM-7B-SFT
FastData-LM-7B-SFT is a specialized language model fine-tuned on the 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:
unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit - Fine-Tuning Dataset:
thlurte/VSG-lite-1.5k - Primary Domain: Vectorized Synthetic Data Generation across 13 industrial schemas.
- Strict Anti-Loop Constraint: Eliminates
.apply(),.iterrows(), and allfor/whilerow-level iteration.
Usage Example
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "thlurte/FastData-LM-7B-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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from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "thlurte/FastData-LM-7B-SFT")