Base model: Qwen/Qwen2.5-Coder-1.5B-Instruct
Method: LoRA (Low-Rank Adaptation) via LLaMA-Factory
Dataset: tkeskin/leetcode-solutions (instruct config) — directed C++/Java/Python translation pairs derived from LeetCode solutions
Hardware: AMD MI210 (ROCm) / NVIDIA CUDA, flash_attn: sdpa
LoRA target: all linear layers (lora_target: all)
Precision: bf16

Evaluation

Evaluated with an execution-based translation benchmark: each held-out evaluation-config payload from 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.

	Base (Qwen2.5-Coder-1.5B-Instruct)	This model	Δ
pass@1	29.3%	61.9%	+32.6
compile rate	59.6%	84.5%	+24.9

pass@1 by language pair × difficulty (%):

source	target	difficulty	base	this model
cpp	java	Easy	41.4	81.4
cpp	java	Hard	12.7	47.5
cpp	java	Medium	27.9	69.4
cpp	python	Easy	40.7	76.7
cpp	python	Hard	29.8	45.0
cpp	python	Medium	38.6	66.6
java	cpp	Easy	39.5	85.0
java	cpp	Hard	32.8	47.1
java	cpp	Medium	40.0	68.5
java	python	Easy	18.6	78.5
java	python	Hard	15.3	45.8
java	python	Medium	22.7	66.6
python	cpp	Easy	25.9	72.1
python	cpp	Hard	14.3	22.7
python	cpp	Medium	25.9	57.8
python	java	Easy	44.1	62.8
python	java	Hard	10.2	24.6
python	java	Medium	28.7	54.7

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 repo (Stage 5).

Standard benchmarks and the specialization trade-off

Translation specialization has a measurable cost on standard benchmarks (base → this model):

Held-out perplexity on the translation test set drops 1.29 → 1.07 — training fit the target distribution.
General ability is preserved — MMLU is flat (0.512 → 0.514); lm-eval reasoning tasks unchanged within noise.
Code generation from a natural-language spec regresses — HumanEval pass@1 (Python) 75% → 53%.

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.

Intended use

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.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tkeskin/qwen2.5-coder-1.5b-code-translation"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [
    {
        "role": "user",
        "content": "Translate the following C++ code to Python:\n\nint add(int a, int b) { return a + b; }"
    }
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))