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README.md

@@ -1,22 +1,44 @@
 ---
+license: apache-2.0
 base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
 tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2
-- trl
-license: apache-2.0
-language:
-- en
+- code
+- python
+- synthetic-data
+- numpy
+- pandas
+- vectorized
+library_name: peft
+pipeline_tag: text-generation
 ---
 
-# Uploaded  model
+# FastData-LM-7B-SFT
+
+**FastData-LM-7B-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**: [`unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit`](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit)
+- **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
 
-- **Developed by:** gnoril
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "thlurte/FastData-LM-7B-SFT",
+    max_seq_length = 2048,
+    load_in_4bit = True,
+)
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
+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."}
+]
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+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))
+```