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Model: tkeskin/qwen2.5-coder-1.5b-code-translation
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+license: apache-2.0
+datasets:
+  - tkeskin/leetcode-solutions
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - lora
+  - llama-factory
+  - code
+  - code-translation
+  - qwen
+  - qwen2
+  - code
+---
+
+# qwen2.5-coder-1.5b-code-translation
+
+A fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) for translating code between **C++**, **Java**, and **Python**.
+
+## Training
+
+- **Base model:** Qwen/Qwen2.5-Coder-1.5B-Instruct
+- **Method:** LoRA (Low-Rank Adaptation) via [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
+- **Dataset:** [tkeskin/leetcode-solutions](https://huggingface.co/datasets/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](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.
+
+| | 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](https://github.com/tkeskin/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))
+```