qwen2.5-coder-abap/README.md

---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
  - abap
  - sap
  - code
  - orpo
  - fine-tuned
  - qwen2
language:
  - en
pipeline_tag: text-generation
library_name: transformers
---

# Qwen-Coder-ABAP

Fine-tuned [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) for **modern ABAP 7.4+ code generation**.

Trained using **ORPO (Odds Ratio Preference Optimization)** on a high-quality dataset of 280 ABAP preference pairs to promote modern syntax and eliminate legacy patterns.

## Model Details

| Attribute | Value |
|-----------|-------|
| Base Model | Qwen2.5-Coder-7B-Instruct |
| Fine-tuning Method | ORPO |
| Training Examples | 280 preference pairs |
| LoRA Rank | 32 |
| LoRA Alpha | 64 |
| Training Epochs | 3 |
| Hardware | NVIDIA RTX 4060 Ti 16GB |

## Performance

Benchmarked on 12 ABAP coding tasks (modernization, basic coding, completion):

| Metric | Base Model | Fine-tuned | Improvement |
|--------|------------|------------|-------------|
| Modern ABAP patterns | 18 | 23 | +28% |
| Legacy patterns | 7 | 2 | -71% |
| Net score | +11 | +21 | +91% |
| Inference time | 74.7s | 23.5s | 3x faster |

## Usage

### Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("oisee/qwen-coder-abap")
tokenizer = AutoTokenizer.from_pretrained("oisee/qwen-coder-abap")

messages = [
    {"role": "system", "content": "You are an ABAP programming assistant specialized in modern ABAP 7.4+ syntax."},
    {"role": "user", "content": "Convert this to modern ABAP: READ TABLE lt_data INTO ls_row WITH KEY id = 1."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Ollama

```bash
ollama run oisee/qwen-coder-abap "Convert READ TABLE to modern ABAP"
```

Also available as quantized GGUF: [ollama.com/oisee/qwen-coder-abap](https://ollama.com/oisee/qwen-coder-abap)

## Modern ABAP Patterns (Promoted)

The model is trained to prefer these modern ABAP 7.4+ patterns:

```abap
" Inline declarations
DATA(lv_result) = calculate_total( ).
FIELD-SYMBOL(<ls_row>) TYPE ty_row.

" Table expressions (instead of READ TABLE)
DATA(ls_customer) = lt_customers[ id = '12345' ].

" NEW operator (instead of CREATE OBJECT)
DATA(lo_handler) = NEW zcl_handler( iv_config = 'DEFAULT' ).

" String templates (instead of CONCATENATE)
DATA(lv_msg) = |Customer { lv_id } has { lv_count } orders|.

" VALUE constructor
DATA(lt_data) = VALUE #( ( id = 1 name = 'A' ) ( id = 2 name = 'B' ) ).

" REDUCE for aggregation
DATA(lv_sum) = REDUCE #( INIT s = 0 FOR row IN lt_data NEXT s = s + row-amount ).

" FILTER for table filtering
DATA(lt_active) = FILTER #( lt_data WHERE status = 'A' ).

" Modern LOOP with inline field-symbol
LOOP AT lt_data ASSIGNING FIELD-SYMBOL(<ls_row>).
  <ls_row>-processed = abap_true.
ENDLOOP.
```

## Legacy Patterns (Avoided)

The model learns to avoid these legacy patterns:

```abap
" Legacy - model avoids these
READ TABLE lt_data INTO ls_row WITH KEY id = 1.
CREATE OBJECT lo_handler.
CALL METHOD lo_handler->process.
CONCATENATE lv_a lv_b INTO lv_result.
MOVE lv_source TO lv_target.
MOVE-CORRESPONDING ls_source TO ls_target.
DATA: lv_var TYPE string.  " Colon syntax
```

## Training Dataset

The ORPO training dataset contains **280 high-quality preference pairs** covering:

| Category | Examples | Patterns |
|----------|----------|----------|
| Constructor Expressions | 45 | VALUE #, NEW #, CORRESPONDING #, COND #, SWITCH #, REDUCE |
| Inline Declarations | 30 | DATA(), FIELD-SYMBOL(), @DATA for SELECT |
| String Templates | 25 | \|text { var }\| with formatting |
| Table Expressions | 35 | lt_table[ key = value ], OPTIONAL, DEFAULT |
| Modern SELECT | 25 | @DATA, INTO TABLE @, host variables |
| Exception Handling | 15 | TRY/CATCH with cx_root |
| AMDP/HANA | 12 | AMDP procedures, table functions |
| RAP/BDEF | 10 | Behavior definitions, draft handling |
| ALV/SALV | 15 | CL_SALV_TABLE patterns |
| Unit Testing | 18 | cl_abap_unit_assert patterns |
| Other | 50 | JSON, HTTP, File operations, BAL logging |

Each example contains:
- `prompt`: The coding task
- `chosen`: Modern ABAP solution (preferred)
- `rejected`: Legacy ABAP equivalent (discouraged)

## Training Configuration

```python
# ORPO Config
ORPOConfig(
    max_length=1536,
    beta=0.1,  # ORPO penalty strength
    learning_rate=8e-6,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=8,
    num_train_epochs=3,
    optim="adamw_8bit",
)

# LoRA Config
r=32, lora_alpha=64, lora_dropout=0.05
target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
                "gate_proj", "up_proj", "down_proj"]
```

## Limitations

- Focused on ABAP 7.4+ syntax; may not cover all SAP-specific APIs
- Training data is synthetic; real-world edge cases may vary
- Best for code modernization and generation tasks
- 7B parameter model; larger models may produce higher quality for complex tasks

## License

Apache 2.0 (inherited from [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct))

## Citation

```bibtex
@misc{qwen-coder-abap,
  author = {oisee},
  title = {Qwen-Coder-ABAP: Fine-tuned Qwen2.5-Coder for Modern ABAP},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/oisee/qwen-coder-abap}
}
```

## Acknowledgments

- [Qwen Team](https://github.com/QwenLM) for Qwen2.5-Coder
- [Unsloth](https://github.com/unslothai/unsloth) for efficient fine-tuning
- [TRL](https://github.com/huggingface/trl) for ORPO implementation