qwen-3.5-0.8b-code-translation

A fine-tuned version of Qwen/Qwen3.5-0.8B for translating code between C++, Java, and Python.

Training

• Base model: Qwen/Qwen3.5-0.8B
• 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
• Template: qwen3_5_nothink (non-thinking mode — direct code output, no chain-of-thought)
• LoRA target: all linear layers (lora_target: all)
• Precision: bf16

Evaluation

Evaluated with the same execution-based translation benchmark as the rest of this series: each held-out evaluation-config payload from tkeskin/leetcode-solutions is compiled and run against its input/output pairs. Held out from training (no leakage). Metric is pass@1, n-weighted over 3,336 payloads.

This fine-tune did not improve translation performance over the base model — pass@1 is essentially unchanged, fractionally lower (within noise):

	Base (Qwen3.5-0.8B)	This model	Δ
pass@1	16.2%	15.7%	−0.5
compile rate	49.5%	49.9%	+0.4

For contrast, the same LoRA recipe and dataset lifted a larger code-pretrained base (Qwen2.5-Coder-1.5B) from 29.3% to 61.9% pass@1. The benefit appears to depend on model scale and code pre-training; at 0.8B, on this unusual architecture, the recipe did not transfer. We publish this result as-is rather than omit it.

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

source	target	difficulty	base	this model
cpp	java	Easy	11.7	13.8
cpp	java	Hard	2.5	2.5
cpp	java	Medium	10.1	8.5
cpp	python	Easy	22.1	18.0
cpp	python	Hard	11.5	9.2
cpp	python	Medium	18.8	18.5
java	cpp	Easy	14.3	17.7
java	cpp	Hard	1.7	4.2
java	cpp	Medium	12.2	10.0
java	python	Easy	36.6	34.9
java	python	Hard	16.0	10.7
java	python	Medium	27.9	27.3
python	cpp	Easy	40.1	38.1
python	cpp	Hard	7.6	10.1
python	cpp	Medium	20.7	24.1
python	java	Easy	13.1	11.0
python	java	Hard	0.8	0.8
python	java	Medium	5.4	4.3

Full methodology is in the llm-fine-tune repo (Stage 5).

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.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tkeskin/qwen-3.5-0.8b-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))