Instructions to use Adityapande403/embeddie-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Adityapande403/embeddie-coder with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Adityapande403/embeddie-coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Adityapande403/embeddie-coder")
model = AutoModelForCausalLM.from_pretrained("Adityapande403/embeddie-coder", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Adityapande403/embeddie-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Adityapande403/embeddie-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Adityapande403/embeddie-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Adityapande403/embeddie-coder

SGLang

How to use Adityapande403/embeddie-coder with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Adityapande403/embeddie-coder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Adityapande403/embeddie-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Adityapande403/embeddie-coder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Adityapande403/embeddie-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Adityapande403/embeddie-coder with Docker Model Runner:
```
docker model run hf.co/Adityapande403/embeddie-coder
```

embeddie-coder / README.md

Adityapande403

feat: upload EmbeddieCoder AI 1.5B merged model, tokenizer, and LoRA adapter

9699b55 verified 2 days ago

preview code

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6.07 kB

---
language:
- en
license: apache-2.0
tags:
- code
- c
- cpp
- embedded
- firmware
- microcontrollers
- rtos
- automotive
- telematics
- freertos
- stm32
- esp32
- modbus
- can-bus
- ais-140
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
---
# EmbeddieCoder AI (1.5B)
**Universal Embedded Systems Engineering Small Language Model**

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE)
[![GitHub](https://img.shields.io/badge/GitHub-adityapande403%2FEmbeddie--coder-black.svg)](https://github.com/adityapande403/Embeddie-coder)
[![Base Model](https://img.shields.io/badge/Base_Model-Qwen2.5--Coder--1.5B-purple.svg)](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)

EmbeddieCoder AI is an offline-first, domain-specialized Small Language Model (SLM) for embedded systems engineering, firmware architecture, real-time operating systems (RTOS), industrial communication protocols, and hardware diagnostic triage.

---

## 1. Core Engineering Principle
> **"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:
1. **Deterministic Protocol Decoders:** Modbus RTU/TCP, SAE J1939, OBD-II, and AIS-140 telematics.
2. **Deterministic Sandboxed Verification:** Sandboxed GCC 16.2.0 compilation with `-Wall -Wextra -Werror -std=c11`.
3. **Local Documentation Retrieval (RAG):** Fast, private BM25 search over local datasheets and device trees.
4. **Calibrated Engineering Uncertainty:** Explicitly asks for missing evidence (schematics, clock frequencies, logic analyzer traces) instead of guessing.

---

## 2. Universal Embedded Taxonomy
The model covers 9 core embedded domains:
1. **Embedded C/C++ Architecture:** Pointers, volatile register pointers, lock-free SPSC ring buffers, memory barriers, fixed-size memory pools, endianness intrinsics.
2. **Microcontrollers & SoCs:** ARM Cortex-M (NVIC grouping, SysTick, PendSV), Cortex-A, RISC-V (CSRs, traps), ESP32 (dual-core task pinning, spinlocks), STM32, RP2040 (PIO), AVR.
3. **Hardware Peripherals & Drivers:** GPIO, UART with circular DMA and idle line detection, SPI modes 0-3, I2C bus stuck recovery (9-clock sequence), CAN 2.0B / CAN FD, PWM, Timers, Watchdogs (IWDG/WWDG).
4. **Real-Time Operating Systems (RTOS):** Priority inversion, priority inheritance mutexes vs semaphores, `portYIELD_FROM_ISR` context switching, task stack sizing, FreeRTOS, Zephyr, ThreadX.
5. **Embedded Linux:** Device tree nodes (`.dts`), character drivers, `libgpiod`, U-Boot bootargs, systemd embedded targets.
6. **Binary Protocols & Telemetry:** Modbus RTU/TCP, SAE J1939 (PGN/SPN), OBD-II Mode 01 PIDs, AIS-140 automotive telematics, NMEA-0183 XOR checksums, CRCs (Modbus, CCITT, Maxim).
7. **Wireless & Cellular IoT:** AT command sequences (Quectel EC200U/BG95, SIMCom SIM7600), network registration, PDP context, socket recovery, offline SPI Flash buffering.
8. **Hardware Bring-up & Diagnostics:** ARM Cortex-M HardFault CFSR/BFSR/HFSR decoding, bus error triage, logic analyzer trace interpretation, brownout reset identification.
9. **Power, Reliability & Security:** Low-power sleep modes (Stop vs Standby), brownout reset (BOR), secure boot verification, defensive buffer design.

---

## 3. Measured Benchmark Results

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.*

---

## 4. Hardware Resource & Performance Footprint

Measured on an Intel Core i7-14700K with NVIDIA GeForce RTX 5060 Ti 16 GB:
- **Model Cold Start:** **5.09 seconds**
- **Peak GPU VRAM:** **2,944.4 MB** (~2.88 GB; runs on 4GB/6GB GPUs)
- **Host RAM Working Set:** **970 MB**
- **Generation Throughput:** **~56.2 - 59.8 tokens/sec**
- **SFT Training Time:** **19.56 seconds** (5 epochs, 10 steps, Peak VRAM: 3.2 GB)

---

## 5. Usage

### 5.1 Hugging Face Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import 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))
```

### 5.2 Ollama Deployment
Use the included `Modelfile`:
```bash
ollama create embeddie-coder -f Modelfile
ollama run embeddie-coder
```

---

## 6. Full Code Repository
The complete training pipeline, deterministic verification tools, GCC sandbox, and benchmark suite are available at:
👉 **[https://github.com/adityapande403/Embeddie-coder](https://github.com/adityapande403/Embeddie-coder)**

	---
	language:
	- en
	license: apache-2.0
	tags:
	- code
	- c
	- cpp
	- embedded
	- firmware
	- microcontrollers
	- rtos
	- automotive
	- telematics
	- freertos
	- stm32
	- esp32
	- modbus
	- can-bus
	- ais-140
	base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
	pipeline_tag: text-generation
	library_name: transformers
	---

	# EmbeddieCoder AI (1.5B)
	Universal Embedded Systems Engineering Small Language Model

	[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE)
	[![GitHub](https://img.shields.io/badge/GitHub-adityapande403%2FEmbeddie--coder-black.svg)](https://github.com/adityapande403/Embeddie-coder)
	[![Base Model](https://img.shields.io/badge/Base_Model-Qwen2.5--Coder--1.5B-purple.svg)](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)

	EmbeddieCoder AI is an offline-first, domain-specialized Small Language Model (SLM) for embedded systems engineering, firmware architecture, real-time operating systems (RTOS), industrial communication protocols, and hardware diagnostic triage.

	---

	## 1. Core Engineering Principle
	> "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:
	1. Deterministic Protocol Decoders: Modbus RTU/TCP, SAE J1939, OBD-II, and AIS-140 telematics.
	2. Deterministic Sandboxed Verification: Sandboxed GCC 16.2.0 compilation with `-Wall -Wextra -Werror -std=c11`.
	3. Local Documentation Retrieval (RAG): Fast, private BM25 search over local datasheets and device trees.
	4. Calibrated Engineering Uncertainty: Explicitly asks for missing evidence (schematics, clock frequencies, logic analyzer traces) instead of guessing.

	---

	## 2. Universal Embedded Taxonomy
	The model covers 9 core embedded domains:
	1. Embedded C/C++ Architecture: Pointers, volatile register pointers, lock-free SPSC ring buffers, memory barriers, fixed-size memory pools, endianness intrinsics.
	2. Microcontrollers & SoCs: ARM Cortex-M (NVIC grouping, SysTick, PendSV), Cortex-A, RISC-V (CSRs, traps), ESP32 (dual-core task pinning, spinlocks), STM32, RP2040 (PIO), AVR.
	3. Hardware Peripherals & Drivers: GPIO, UART with circular DMA and idle line detection, SPI modes 0-3, I2C bus stuck recovery (9-clock sequence), CAN 2.0B / CAN FD, PWM, Timers, Watchdogs (IWDG/WWDG).
	4. Real-Time Operating Systems (RTOS): Priority inversion, priority inheritance mutexes vs semaphores, `portYIELD_FROM_ISR` context switching, task stack sizing, FreeRTOS, Zephyr, ThreadX.
	5. Embedded Linux: Device tree nodes (`.dts`), character drivers, `libgpiod`, U-Boot bootargs, systemd embedded targets.
	6. Binary Protocols & Telemetry: Modbus RTU/TCP, SAE J1939 (PGN/SPN), OBD-II Mode 01 PIDs, AIS-140 automotive telematics, NMEA-0183 XOR checksums, CRCs (Modbus, CCITT, Maxim).
	7. Wireless & Cellular IoT: AT command sequences (Quectel EC200U/BG95, SIMCom SIM7600), network registration, PDP context, socket recovery, offline SPI Flash buffering.
	8. Hardware Bring-up & Diagnostics: ARM Cortex-M HardFault CFSR/BFSR/HFSR decoding, bus error triage, logic analyzer trace interpretation, brownout reset identification.
	9. Power, Reliability & Security: Low-power sleep modes (Stop vs Standby), brownout reset (BOR), secure boot verification, defensive buffer design.

	---

	## 3. Measured Benchmark Results

	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.*

	---

	## 4. Hardware Resource & Performance Footprint

	Measured on an Intel Core i7-14700K with NVIDIA GeForce RTX 5060 Ti 16 GB:
	- Model Cold Start: 5.09 seconds
	- Peak GPU VRAM: 2,944.4 MB (~2.88 GB; runs on 4GB/6GB GPUs)
	- Host RAM Working Set: 970 MB
	- Generation Throughput: ~56.2 - 59.8 tokens/sec
	- SFT Training Time: 19.56 seconds (5 epochs, 10 steps, Peak VRAM: 3.2 GB)

	---

	## 5. Usage

	### 5.1 Hugging Face Transformers
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import 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))
	```

	### 5.2 Ollama Deployment
	Use the included `Modelfile`:
	```bash
	ollama create embeddie-coder -f Modelfile
	ollama run embeddie-coder
	```

	---

	## 6. Full Code Repository
	The complete training pipeline, deterministic verification tools, GCC sandbox, and benchmark suite are available at:
	👉 [https://github.com/adityapande403/Embeddie-coder](https://github.com/adityapande403/Embeddie-coder)