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--- |
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license: apache-2.0 |
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language: c++ |
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tags: |
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- code-generation |
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- codellama |
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- peft |
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- unit-tests |
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- causal-lm |
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- text-generation |
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base_model: codellama/CodeLlama-7b-hf |
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model_type: llama |
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pipeline_tag: text-generation |
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--- |
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# 🧪 CodeLLaMA Unit Test Generator (Merged Model) |
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This is a merged model that combines [`codellama/CodeLlama-7b-hf`](https://huggingface.co/codellama/CodeLlama-7b-hf) with LoRA fine-tuning trained on a dataset of embedded C/C++ functions and corresponding unit tests. |
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It specializes in generating **comprehensive unit tests** for C/C++ code using frameworks like **GoogleTest** or **CppUTest**, focusing on: |
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- Edge cases |
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- Boundary conditions |
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- Exception handling |
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- MISRA C compliance (if applicable) |
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--- |
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## 📌 Example Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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model = AutoModelForCausalLM.from_pretrained("Utkarsh524/codellama_utests_full") |
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tokenizer = AutoTokenizer.from_pretrained("Utkarsh524/codellama_utests_full") |
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prompt = "<|system|>\nGenerate comprehensive unit tests for C/C++ code. Cover all edge cases, boundary conditions, and error scenarios.\nOutput Constraints:\n1. ONLY include test code (no explanations, headers, or main functions)\n2. Start directly with TEST(...)\n3. End after last test case\n4. Never include framework boilerplate\n<|user|>\nCreate tests for:\nint add(int a, int b) { return a + b; }\n<|assistant|>\n" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=512) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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