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Model: oisee/qwen2.5-coder-abap Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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tags:
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- abap
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- sap
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- code
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- orpo
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- fine-tuned
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- qwen2
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Qwen-Coder-ABAP
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Fine-tuned [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) for **modern ABAP 7.4+ code generation**.
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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.
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## Model Details
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| Attribute | Value |
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|-----------|-------|
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| Base Model | Qwen2.5-Coder-7B-Instruct |
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| Fine-tuning Method | ORPO |
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| Training Examples | 280 preference pairs |
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| LoRA Rank | 32 |
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| LoRA Alpha | 64 |
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| Training Epochs | 3 |
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| Hardware | NVIDIA RTX 4060 Ti 16GB |
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## Performance
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Benchmarked on 12 ABAP coding tasks (modernization, basic coding, completion):
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| Metric | Base Model | Fine-tuned | Improvement |
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|--------|------------|------------|-------------|
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| Modern ABAP patterns | 18 | 23 | +28% |
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| Legacy patterns | 7 | 2 | -71% |
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| Net score | +11 | +21 | +91% |
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| Inference time | 74.7s | 23.5s | 3x faster |
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## Usage
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("oisee/qwen-coder-abap")
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tokenizer = AutoTokenizer.from_pretrained("oisee/qwen-coder-abap")
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messages = [
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{"role": "system", "content": "You are an ABAP programming assistant specialized in modern ABAP 7.4+ syntax."},
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{"role": "user", "content": "Convert this to modern ABAP: READ TABLE lt_data INTO ls_row WITH KEY id = 1."}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Ollama
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```bash
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ollama run oisee/qwen-coder-abap "Convert READ TABLE to modern ABAP"
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```
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Also available as quantized GGUF: [ollama.com/oisee/qwen-coder-abap](https://ollama.com/oisee/qwen-coder-abap)
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## Modern ABAP Patterns (Promoted)
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The model is trained to prefer these modern ABAP 7.4+ patterns:
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```abap
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" Inline declarations
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DATA(lv_result) = calculate_total( ).
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FIELD-SYMBOL(<ls_row>) TYPE ty_row.
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" Table expressions (instead of READ TABLE)
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DATA(ls_customer) = lt_customers[ id = '12345' ].
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" NEW operator (instead of CREATE OBJECT)
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DATA(lo_handler) = NEW zcl_handler( iv_config = 'DEFAULT' ).
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" String templates (instead of CONCATENATE)
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DATA(lv_msg) = |Customer { lv_id } has { lv_count } orders|.
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" VALUE constructor
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DATA(lt_data) = VALUE #( ( id = 1 name = 'A' ) ( id = 2 name = 'B' ) ).
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" REDUCE for aggregation
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DATA(lv_sum) = REDUCE #( INIT s = 0 FOR row IN lt_data NEXT s = s + row-amount ).
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" FILTER for table filtering
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DATA(lt_active) = FILTER #( lt_data WHERE status = 'A' ).
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" Modern LOOP with inline field-symbol
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LOOP AT lt_data ASSIGNING FIELD-SYMBOL(<ls_row>).
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<ls_row>-processed = abap_true.
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ENDLOOP.
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```
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## Legacy Patterns (Avoided)
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The model learns to avoid these legacy patterns:
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```abap
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" Legacy - model avoids these
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READ TABLE lt_data INTO ls_row WITH KEY id = 1.
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CREATE OBJECT lo_handler.
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CALL METHOD lo_handler->process.
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CONCATENATE lv_a lv_b INTO lv_result.
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MOVE lv_source TO lv_target.
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MOVE-CORRESPONDING ls_source TO ls_target.
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DATA: lv_var TYPE string. " Colon syntax
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```
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## Training Dataset
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The ORPO training dataset contains **280 high-quality preference pairs** covering:
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| Category | Examples | Patterns |
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|----------|----------|----------|
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| Constructor Expressions | 45 | VALUE #, NEW #, CORRESPONDING #, COND #, SWITCH #, REDUCE |
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| Inline Declarations | 30 | DATA(), FIELD-SYMBOL(), @DATA for SELECT |
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| String Templates | 25 | \|text { var }\| with formatting |
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| Table Expressions | 35 | lt_table[ key = value ], OPTIONAL, DEFAULT |
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| Modern SELECT | 25 | @DATA, INTO TABLE @, host variables |
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| Exception Handling | 15 | TRY/CATCH with cx_root |
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| AMDP/HANA | 12 | AMDP procedures, table functions |
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| RAP/BDEF | 10 | Behavior definitions, draft handling |
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| ALV/SALV | 15 | CL_SALV_TABLE patterns |
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| Unit Testing | 18 | cl_abap_unit_assert patterns |
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| Other | 50 | JSON, HTTP, File operations, BAL logging |
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Each example contains:
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- `prompt`: The coding task
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- `chosen`: Modern ABAP solution (preferred)
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- `rejected`: Legacy ABAP equivalent (discouraged)
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## Training Configuration
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```python
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# ORPO Config
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ORPOConfig(
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max_length=1536,
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beta=0.1, # ORPO penalty strength
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learning_rate=8e-6,
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per_device_train_batch_size=1,
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gradient_accumulation_steps=8,
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num_train_epochs=3,
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optim="adamw_8bit",
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)
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# LoRA Config
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r=32, lora_alpha=64, lora_dropout=0.05
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj"]
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```
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## Limitations
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- Focused on ABAP 7.4+ syntax; may not cover all SAP-specific APIs
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- Training data is synthetic; real-world edge cases may vary
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- Best for code modernization and generation tasks
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- 7B parameter model; larger models may produce higher quality for complex tasks
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## License
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Apache 2.0 (inherited from [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct))
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## Citation
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```bibtex
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@misc{qwen-coder-abap,
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author = {oisee},
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title = {Qwen-Coder-ABAP: Fine-tuned Qwen2.5-Coder for Modern ABAP},
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year = {2024},
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publisher = {Hugging Face},
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url = {https://huggingface.co/oisee/qwen-coder-abap}
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}
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```
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## Acknowledgments
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- [Qwen Team](https://github.com/QwenLM) for Qwen2.5-Coder
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- [Unsloth](https://github.com/unslothai/unsloth) for efficient fine-tuning
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- [TRL](https://github.com/huggingface/trl) for ORPO implementation
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