Base model: mistralai/Mistral-7B-Instruct-v0.3
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 (Mistral-7B-Instruct-v0.3)	This model	Δ
pass@1	11.8%	59.6%	+47.8
compile rate	45.0%	84.7%	+39.6

This is the largest gain in the series — the base model barely produces compilable C++/Java, and fine-tuning lifts it to near the top. pass@1 by language pair × difficulty (%):

source	target	difficulty	base	this model
cpp	java	Easy	9.7	81.4
cpp	java	Hard	3.4	47.5
cpp	java	Medium	8.1	72.9
cpp	python	Easy	29.7	61.6
cpp	python	Hard	16.0	45.8
cpp	python	Medium	27.3	64.3
java	cpp	Easy	2.7	79.6
java	cpp	Hard	2.5	51.3
java	cpp	Medium	4.1	70.0
java	python	Easy	4.1	64.5
java	python	Hard	6.1	45.8
java	python	Medium	8.4	62.3
python	cpp	Easy	19.7	64.6
python	cpp	Hard	10.1	26.9
python	cpp	Medium	17.8	51.9
python	java	Easy	17.2	64.8
python	java	Hard	2.5	28.8
python	java	Medium	8.5	53.1

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

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tkeskin/mistral-7b-v0.3-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))