Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,78 @@
+---
+language: en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- llama
+- data-management
+- data-engineering
+- migration
+- sql
+- reasoning
+- grpo
+- rlhf
+license: other
+base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+---
+
+# Agentic Data 1
+
+A specialized 8B reasoning model fine-tuned for Data Management, Data Engineering, and Migration tasks.
+
+## Model Details
+
+- **Base**: DeepSeek-R1-Distill-Llama-8B
+- **Training**: 3-stage pipeline (SFT QLoRA → Doc-Grounded SFT → GRPO Reinforcement Learning)
+- **Format**: BF16 SafeTensors (PyTorch / HuggingFace Transformers compatible)
+- **Parameters**: 8B
+
+## Training Pipeline
+
+| Stage | Method | Data | Hardware |
+|-------|--------|------|----------|
+| Stage 1 | QLoRA SFT (3 versions) | 14,666 synthetic pairs + 7,558 doc-grounded chunks | Apple Silicon M-Series |
+| Stage 2 | GRPO Reinforcement Learning | 100 reasoning prompts with reward functions | NVIDIA H100 80GB |
+
+## Capabilities
+
+- **SQL Dialect Conversion**: Oracle ↔ PostgreSQL ↔ T-SQL ↔ Snowflake ↔ BigQuery ↔ Databricks
+- **ETL Pipeline Migration**: Informatica → dbt, DataStage → Spark, BODS → Airflow
+- **Legacy System Modernization**: COBOL, JCL, SAS, ABAP → modern stacks
+- **Data Quality & Governance**: Assessment, validation, and compliance
+- **Migration Lifecycle**: Discovery → Risk → Planning → Conversion → Verification
+- **Step-by-Step Reasoning**: Uses `<think>...</think>` tags for chain-of-thought reasoning
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "DataManagement-AI/Agentic-Data-1",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("DataManagement-AI/Agentic-Data-1")
+
+messages = [
+    {"role": "system", "content": "You are Agentic Data 1, an expert data management and migration reasoning model. Think step-by-step before answering."},
+    {"role": "user", "content": "Convert this Oracle PL/SQL stored procedure to PostgreSQL PL/pgSQL."}
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
+outputs = model.generate(inputs, max_new_tokens=1500)
+print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+## Benchmarks (SFT V3)
+
+| Metric | Base Model | Agentic Data 1 | Improvement |
+|--------|-----------|-----------------|-------------|
+| Overall Score | 0.554 | **0.636** | +14.8% |
+| Implementation Quality | 0.584 | **0.761** | +30.3% |
+| Think-Tag Rate | 0% | **100%** | ∞ |
+| Reasoning Quality | 0.534 | **0.622** | +16.5% |
+
+## License
+
+For research and educational purposes.