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Model: oisee/qwen2.5-coder-abap
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---
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

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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31
special_tokens_map.json Normal file
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{
"additional_special_tokens": [
"<|im_start|>",
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"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
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"<|vision_end|>",
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],
"eos_token": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|PAD_TOKEN|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

3
tokenizer.json Normal file
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217
tokenizer_config.json Normal file
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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
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"special": true
},
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"padding_side": "left",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
}

1
vocab.json Normal file

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