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anyze
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volatile, mutex vs semaphore, priority inversion, DMA,
watchdogs, memory sections, Cortex-M interrupts/faults.grep/read, live facts (versions, prices, errata,
CVEs) and precise spec values to web_search; answers well-known concepts directly.Embedded C
Set up CAN1 on an STM32 at 500 kbit/s.Write a UART RX interrupt handler with a ring buffer.Why is my log output garbled at 115200 baud?Automotive / AUTOSAR / UDS
Explain how CAN bus arbitration decides message priority.What is the AUTOSAR RTE and what does it sit between?Walk me through a UDS flashing sequence.Concepts
What does the volatile keyword guarantee, and what does it NOT guarantee?What is priority inversion and how does an RTOS prevent it?General coding & shell
Write a Python script to parse a CSV and summarize one column.How do I find and kill the process using a given port on Linux?Install uv and create a Python venv on Windows PowerShell.Agentic (with tools provided)
What is the newest stable Zephyr RTOS release? → calls web_searchWhere is our CAN receive ISR defined? → calls grepThe model emits tool calls as a ```json code block and then stops:
markdown
```json{"name": "web_search", "arguments": {"query": "Zephyr RTOS latest stable LTS release version"}}```
Describe the available tools in the system prompt using this exact framing:
text
{YOUR SYSTEM PROMPT}# ToolsYou may call one or more functions to assist with the user query.You are provided with function signatures within <tools></tools> XML tags:<tools>{"type": "function", "function": {"name": "web_search", "description": "Search the web for current information.", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "the search query"}}, "required": ["query"]}}}</tools>To call a function, output a ```json code block containing a JSON object with thefunction name and arguments, then stop:```json{"name": <function-name>, "arguments": <args-json-object>}
markdown
Your harness parses the block, executes the tool, and returns the result as a **user**message wrapped in `<tool_response>...</tool_response>`; the model then answers from it.**Agent mode (THINK → ACT):** append this to the system prompt for a one-sentencerationale before each call, plus the verify/restraint policy:```textWhen working autonomously, think first: give your reasoning in one short sentence, then act. Call a tool only when you genuinely cannot answer from your own knowledge or the codebase — explain well-known concepts and definitions directly, with no tool call. Use grep or read for the user's own code; use web_search for external facts that change over time (latest versions, prices, errata, CVEs) and never guess such a fact. Also VERIFY precise specification values you are not certain of — exact service IDs, rule numbers, register addresses, thresholds, timing figures — with web_search instead of answering from memory; if you cannot verify, say so explicitly. Emit at most one tool call, then stop.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel = AutoModelForCausalLM.from_pretrained("anyze/Ze1.5-Automotive-Embedded-Instruct",torch_dtype="auto", device_map="auto")tok = AutoTokenizer.from_pretrained("anyze/Ze1.5-Automotive-Embedded-Instruct")messages = [{"role": "system", "content": "You are Ze1.5, an embedded-systems and automotive ""firmware specialist: C/C++, MCUs, RTOS, drivers/peripherals (UART/SPI/I2C/CAN/LIN/""Ethernet), ISRs, UDS/OBD diagnostics, MISRA C, and AUTOSAR (Classic and Adaptive ""Platform). Answer precisely and, when a tool is provided and useful, call it."},{"role": "user", "content": "Set up CAN1 on an STM32 at 500 kbit/s."},]text = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)out = model.generate(**tok(text, return_tensors="pt").to(model.device), max_new_tokens=512)print(tok.decode(out[0], skip_special_tokens=True))
Suggested sampling: temperature 0.7, top_p 0.8, top_k 20, repetition_penalty 1.1
(as shipped in generation_config.json), or greedy for deterministic tool calls.
A ready F16 GGUF is provided. Ollama — create a Modelfile:
markdown
FROM ./Ze1.5-1.5B-Automotive-Embedded-Instruct-F16.ggufSYSTEM """You are Ze1.5, an embedded-systems and automotive firmware specialist: C/C++, MCUs, RTOS, drivers/peripherals (UART/SPI/I2C/CAN/LIN/Ethernet), ISRs, UDS/OBD diagnostics, MISRA C, and AUTOSAR (Classic and Adaptive Platform). Answer precisely and, when a tool is provided and useful, call it."""PARAMETER temperature 0.7PARAMETER top_p 0.8PARAMETER top_k 20PARAMETER repeat_penalty 1.1
bash
ollama create ze1_5-embedded -f Modelfileollama run ze1_5-embedded "Write a ring buffer in C for a UART RX ISR"
LM Studio — load the GGUF; the ChatML-style chat template is embedded, so no manual prompt-format setup is needed. Set the system prompt as above.
web_search when the tool is present; run it with a search tool for factual
work and treat from-memory numbers as unverified.28 layers, hidden 1536, 12 query / 2 KV heads (GQA), FFN 8960 (SwiGLU), RMSNorm, RoPE (θ=1e6, YaRN ×4), tied embeddings, vocab 151,936, context 131,072, 1.54B params.