88 lines
3.3 KiB
Markdown
88 lines
3.3 KiB
Markdown
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---
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base_model: meta-llama/Llama-3.2-1B-Instruct
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license: llama3.2
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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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- llama
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---
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# llama-3.2-1b-instruct-code-translation
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A fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) for translating code between **C++**, **Java**, and **Python**.
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## Training
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- **Base model:** meta-llama/Llama-3.2-1B-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 (Llama-3.2-1B-Instruct) | This model | Δ |
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|---|---|---|---|
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| **pass@1** | 17.5% | **32.5%** | **+15.0** |
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| **compile rate** | 52.8% | **72.7%** | **+19.8** |
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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 | 29.0 | 55.9 |
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| cpp | java | Hard | 7.6 | 24.6 |
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| cpp | java | Medium | 15.9 | 39.5 |
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| cpp | python | Easy | 32.0 | 37.2 |
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| cpp | python | Hard | 10.7 | 17.6 |
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| cpp | python | Medium | 24.4 | 33.8 |
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| java | cpp | Easy | 15.0 | 61.2 |
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| java | cpp | Hard | 4.2 | 27.7 |
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| java | cpp | Medium | 14.4 | 44.4 |
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| java | python | Easy | 31.4 | 44.8 |
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| java | python | Hard | 13.0 | 22.9 |
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| java | python | Medium | 19.2 | 31.8 |
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| python | cpp | Easy | 23.8 | 40.1 |
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| python | cpp | Hard | 1.7 | 6.7 |
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| python | cpp | Medium | 15.6 | 23.3 |
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| python | java | Easy | 24.1 | 30.3 |
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| python | java | Hard | 3.4 | 7.6 |
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| python | java | Medium | 12.4 | 19.4 |
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Full methodology is in the [llm-fine-tune](https://github.com/tkeskin/llm-fine-tune) repo (Stage 5).
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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.
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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/llama-3.2-1b-instruct-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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