100 lines
4.8 KiB
Markdown
100 lines
4.8 KiB
Markdown
---
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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license: apache-2.0
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datasets:
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- tkeskin/leetcode-solutions
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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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tags:
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- lora
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- llama-factory
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- code
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- code-translation
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- qwen
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- qwen2
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- code
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---
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# qwen2.5-coder-1.5b-code-translation
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A fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) for translating code between **C++**, **Java**, and **Python**.
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## Training
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- **Base model:** Qwen/Qwen2.5-Coder-1.5B-Instruct
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- **Method:** LoRA (Low-Rank Adaptation) via [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
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- **Dataset:** [tkeskin/leetcode-solutions](https://huggingface.co/datasets/tkeskin/leetcode-solutions) (`instruct` config) — directed C++/Java/Python translation pairs derived from LeetCode solutions
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- **Hardware:** AMD MI210 (ROCm) / NVIDIA CUDA, `flash_attn: sdpa`
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- **LoRA target:** all linear layers (`lora_target: all`)
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- **Precision:** bf16
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## Evaluation
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Evaluated with an **execution-based** translation benchmark: each held-out `evaluation`-config payload from [tkeskin/leetcode-solutions](https://huggingface.co/datasets/tkeskin/leetcode-solutions) is a directed source→target translation whose output is compiled and run against the problem's input/output pairs. The eval split is held out from training (no leakage). Metric is **pass@1** (all test cases pass), n-weighted over 3,336 payloads.
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| | Base (Qwen2.5-Coder-1.5B-Instruct) | This model | Δ |
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|---|---|---|---|
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| **pass@1** | 29.3% | **61.9%** | **+32.6** |
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| **compile rate** | 59.6% | **84.5%** | **+24.9** |
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pass@1 by language pair × difficulty (%):
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| source | target | difficulty | base | this model |
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|---|---|---|---|---|
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| cpp | java | Easy | 41.4 | 81.4 |
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| cpp | java | Hard | 12.7 | 47.5 |
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| cpp | java | Medium | 27.9 | 69.4 |
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| cpp | python | Easy | 40.7 | 76.7 |
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| cpp | python | Hard | 29.8 | 45.0 |
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| cpp | python | Medium | 38.6 | 66.6 |
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| java | cpp | Easy | 39.5 | 85.0 |
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| java | cpp | Hard | 32.8 | 47.1 |
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| java | cpp | Medium | 40.0 | 68.5 |
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| java | python | Easy | 18.6 | 78.5 |
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| java | python | Hard | 15.3 | 45.8 |
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| java | python | Medium | 22.7 | 66.6 |
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| python | cpp | Easy | 25.9 | 72.1 |
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| python | cpp | Hard | 14.3 | 22.7 |
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| python | cpp | Medium | 25.9 | 57.8 |
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| python | java | Easy | 44.1 | 62.8 |
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| python | java | Hard | 10.2 | 24.6 |
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| python | java | Medium | 28.7 | 54.7 |
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The base model also redefined the harness-provided `ListNode`/`TreeNode` helper types on ~6% of problems (a compile error); this fine-tune does so on **none**, having learned the dataset's convention. Full methodology is in the [llm-fine-tune](https://github.com/tkeskin/llm-fine-tune) repo (Stage 5).
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### Standard benchmarks and the specialization trade-off
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Translation specialization has a measurable cost on standard benchmarks (base → this model):
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- **Held-out perplexity** on the translation test set drops **1.29 → 1.07** — training fit the target distribution.
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- **General ability is preserved** — MMLU is flat (**0.512 → 0.514**); lm-eval reasoning tasks unchanged within noise.
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- **Code generation _from a natural-language spec_ regresses** — HumanEval pass@1 (Python) **75% → 53%**.
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That regression is mostly **output-format specialization, not lost ability**. Trained on LeetCode solutions — which wrap every answer in `class Solution { ... }` with camelCase methods — the model now answers HumanEval-style prompts in that same idiom, e.g. emitting `class Solution { bool hasCloseElements(...) }` instead of the requested free `has_close_elements(...)`. The logic is frequently correct but mismatches the benchmark's free-function contract (C++ class-wrapping rises from **0% in the base to 78%** here). The model improved at its trained format and regressed on unfamiliar ones, while keeping general knowledge intact.
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## Intended use
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Given source code in one of C++, Java, or Python, the model generates a translation into the target language, following the same logic and structure. The Qwen2.5-Coder base model includes code-specific pre-training across C, C++, Java, Python, and many other languages, giving it a stronger prior for code structure.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "tkeskin/qwen2.5-coder-1.5b-code-translation"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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messages = [
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{
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"role": "user",
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"content": "Translate the following C++ code to Python:\n\nint add(int a, int b) { return a + b; }"
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}
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
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outputs = model.generate(inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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