Create README.md

README.md

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+---
+license: apache-2.0
+language: c++
+tags:
+  - code-generation
+  - codellama
+  - peft
+  - unit-tests
+  - causal-lm
+  - text-generation
+  - lora
+base_model: codellama/CodeLlama-7b-hf
+model_type: llama
+pipeline_tag: text-generation
+---
+
+# 🧪 CodeLLaMA Unit Test Generator — LoRA Adapter (v3)
+
+This is a **LoRA adapter** trained on embedded C/C++ functions and their corresponding unit tests using the [`athrv/Embedded_Unittest2`](https://huggingface.co/datasets/athrv/Embedded_Unittest2) dataset.
+
+The adapter is meant to be used **with `codellama/CodeLlama-7b-hf`** and enhances its ability to generate **production-ready C/C++ unit tests**, especially for embedded systems.
+
+---
+
+## 🚀 Key Improvements in `v3`
+
+- ✅ Enhanced instruction prompt tuning using `<|system|>`, `<|user|>`, `<|assistant|>`
+- 🧹 Stripped out `#include`, `main()` and framework boilerplate from training targets
+- 🔚 Appended `// END_OF_TESTS` to each output to guide model termination
+- 🧠 Fine-tuned with sequence length of 4096 tokens for long-context unit tests
+- 🤖 Optimized for frameworks like **CppUTest** or **GoogleTest**
+
+---
+
+## 🔧 How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+base_model_id = "codellama/CodeLlama-7b-hf"
+adapter_id = "Utkarsh524/codellama_utests_embedded_v3"
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Load base model
+base = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True
+)
+
+# Resize to match tokenizer with special tokens
+base.resize_token_embeddings(len(tokenizer))
+
+# Attach LoRA adapter
+model = PeftModel.from_pretrained(base, adapter_id)
+
+# Prepare prompt
+prompt = """<|system|>
+Generate comprehensive unit tests for C/C++ code. Cover all edge cases, boundary conditions, and error scenarios.
+Output Constraints:
+1. ONLY include test code (no explanations, headers, or main functions)
+2. Start directly with TEST(...)
+3. End after last test case
+4. Never include framework boilerplate
+<|user|>
+Create tests for:
+int factorial(int n) { return (n <= 1) ? 1 : n * factorial(n - 1); }
+<|assistant|>
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512, eos_token_id=tokenizer.convert_tokens_to_ids("// END_OF_TESTS"))
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))