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Adityapande403
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Model Details
Model Provider
Adityapande403
Model Tree
Qwen/Qwen2.5-Coder-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
"Train general engineering capabilities, retrieve detailed technical documentation, and use deterministic tools to verify results."
Rather than hallucinating volatile registers or complex polynomial checksums, EmbeddieCoder AI combines domain-adapted weights with:
-Wall -Wextra -Werror -std=c11.The model covers 9 core embedded domains:
portYIELD_FROM_ISR context switching, task stack sizing, FreeRTOS, Zephyr, ThreadX..dts), character drivers, libgpiod, U-Boot bootargs, systemd embedded targets.Evaluated against an independent, held-out benchmark suite (benchmark_suite.jsonl) with zero leakage into the training set:
| Category | Tasks | Baseline Foundation Model | Fine-Tuned SFT Model | Delta |
|---|---|---|---|---|
| RTOS Concurrency | 1 | 0.0% | 100.0% | +100.0% |
| Hardware Diagnostics (HardFault) | 1 | 0.0% | 100.0% | +100.0% |
| AIS-140 Telematics Protocol | 1 | 100.0% | 100.0% | 0.0% |
| Protocols (CRC / CAN / OBD-II) | 3 | 0.0% | 0.0%* | 0.0% |
| Embedded C Math | 1 | 0.0% | 0.0% | 0.0% |
| Safety & Reliability | 1 | 100.0% | 0.0% | -100.0% |
| OVERALL ACCURACY | 8 | 25.0% | 37.5% | +12.5% |
*Note: EmbeddieCoder AI delegates mathematical CRC computations to its deterministic protocol tool engine.
Measured on an Intel Core i7-14700K with NVIDIA GeForce RTX 5060 Ti 16 GB:
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "Adityapande403/embeddie-coder" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32, device_map="auto") messages = [ {"role": "system", "content": "You are EmbeddieCoder AI, an expert embedded systems engineer."}, {"role": "user", "content": "How do you recover a stuck I2C bus when SDA is held low by a slave device after MCU reset?"}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))Use the included Modelfile:
bash
ollama create embeddie-coder -f Modelfileollama run embeddie-coderThe complete training pipeline, deterministic verification tools, GCC sandbox, and benchmark suite are available at: 👉 https://github.com/adityapande403/Embeddie-coder
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "Adityapande403/embeddie-coder" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32, device_map="auto") messages = [ {"role": "system", "content": "You are EmbeddieCoder AI, an expert embedded systems engineer."}, {"role": "user", "content": "How do you recover a stuck I2C bus when SDA is held low by a slave device after MCU reset?"}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))ollama create embeddie-coder -f Modelfileollama run embeddie-coder