base_model, license, datasets, language, pipeline_tag, library_name, tags

base_model	license	datasets	language	pipeline_tag	library_name	tags
meta-llama/Llama-3.2-1B-Instruct	llama3.2	tkeskin/leetcode-solutions	en	text-generation	transformers	lora llama-factory code code-translation llama

llama-3.2-1b-instruct-code-translation

A fine-tuned version of meta-llama/Llama-3.2-1B-Instruct for translating code between C++, Java, and Python.

Training

• Base model: meta-llama/Llama-3.2-1B-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 (Llama-3.2-1B-Instruct)	This model	Δ
pass@1	17.5%	32.5%	+15.0
compile rate	52.8%	72.7%	+19.8

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

source	target	difficulty	base	this model
cpp	java	Easy	29.0	55.9
cpp	java	Hard	7.6	24.6
cpp	java	Medium	15.9	39.5
cpp	python	Easy	32.0	37.2
cpp	python	Hard	10.7	17.6
cpp	python	Medium	24.4	33.8
java	cpp	Easy	15.0	61.2
java	cpp	Hard	4.2	27.7
java	cpp	Medium	14.4	44.4
java	python	Easy	31.4	44.8
java	python	Hard	13.0	22.9
java	python	Medium	19.2	31.8
python	cpp	Easy	23.8	40.1
python	cpp	Hard	1.7	6.7
python	cpp	Medium	15.6	23.3
python	java	Easy	24.1	30.3
python	java	Hard	3.4	7.6
python	java	Medium	12.4	19.4

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/llama-3.2-1b-instruct-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))