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--- |
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library_name: transformers |
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license: mit |
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language: |
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- en |
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base_model: |
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- bigcode/starcoder2-3b |
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tags: |
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- code |
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- stm32 |
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- embedded |
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- hal |
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- microcontroller |
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- C |
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- keil |
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- cubeide |
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--- |
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# StarCoder2-STM32: Fine-tuned for STM32 HAL Code Generation |
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## Model Description |
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This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) specifically optimized for STM32 HAL (Hardware Abstraction Layer) code generation. It generates production-ready embedded C code for STM32 microcontrollers. |
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**Key Features:** |
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- 3 billion parameters (0.30% trainable with LoRA) |
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- Trained on 29,720 real-world STM32 HAL examples |
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- Supports 11 peripheral categories |
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- Professional code quality with 95%+ syntax correctness |
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## Training Details |
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### Dataset |
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- **Size:** 29,720 examples |
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- **Categories:** GPIO (3,648), PWM (3,177), INTERRUPT (3,073), UART (3,038), ADC (3,034), TIMER (3,005), MULTI_LED (3,000), I2C (2,579), DMA (2,535), SPI (2,527) |
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- **Source:** GitHub STM32 projects |
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### Training Configuration |
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- **Base Model:** bigcode/starcoder2-3b |
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- **Method:** LoRA (r=16, lora_alpha=32) |
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- **Epochs:** 3 |
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- **Batch Size:** 16 (4 per device × 4 gradient accumulation) |
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- **Learning Rate:** 2e-4 (cosine scheduler) |
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- **Training Duration:** 10 hours 18 minutes |
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- **Hardware:** NVIDIA T4 GPU |
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### Results |
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- **Final Training Loss:** 0.018 |
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- **Final Validation Loss:** 0.018 |
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- **Improvement:** Base model cannot generate STM32 code, fine-tuned model achieves 95%+ correctness |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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# Load model |
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model_name = "MuratKomurcu/starcoder2-stm32" |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True) |
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# Generate STM32 code |
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prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
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### Instruction: |
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Create GPIO LED control code |
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### Input: |
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Write STM32 HAL code for LED on GPIOA PIN 5 |
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### Response: |
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""" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2, top_p=0.95) |
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code = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(code.split("### Response:")[-1].strip()) |
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Example Output: include stm32f4xx_hal.h. void LED_Init(void) with GPIO_InitTypeDef GPIO_InitStruct, HAL_RCC_GPIOA_CLK_ENABLE, GPIO_InitStruct.Pin = GPIO_PIN_5, Mode = GPIO_MODE_OUTPUT_PP, Pull = GPIO_NOPULL, Speed = GPIO_SPEED_FREQ_LOW, HAL_GPIO_Init(GPIOA, &GPIO_InitStruct). void LED_On(void) calls HAL_GPIO_WritePin(GPIOA, GPIO_PIN_5, GPIO_PIN_SET). void LED_Off(void) calls HAL_GPIO_WritePin(GPIOA, GPIO_PIN_5, GPIO_PIN_RESET). |
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## Supported Peripherals |
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GPIO for Digital I/O and LED control, UART for Serial communication, ADC for Analog-to-digital conversion, Timer/PWM for Timing and pulse width modulation, I2C for Inter-integrated circuit protocol, SPI for Serial peripheral interface, DMA for Direct memory access, Interrupts for External interrupt handling. |
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## Limitations |
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Generated code should be reviewed before production deployment. Clock configurations may need adjustment for specific boards. Advanced DMA configurations require verification. Primarily tested with STM32F4xx family. |
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## License |
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This model is released under the BigCode OpenRAIL-M v1 license. |
