Instructions to use anyze/Ze1.5-Automotive-Embedded-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anyze/Ze1.5-Automotive-Embedded-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="anyze/Ze1.5-Automotive-Embedded-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anyze/Ze1.5-Automotive-Embedded-Instruct")
model = AutoModelForCausalLM.from_pretrained("anyze/Ze1.5-Automotive-Embedded-Instruct")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use anyze/Ze1.5-Automotive-Embedded-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="anyze/Ze1.5-Automotive-Embedded-Instruct",
	filename="gguf/Ze1.5-1.5B-Automotive-Embedded-Instruct-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use anyze/Ze1.5-Automotive-Embedded-Instruct with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16
# Run inference directly in the terminal:
llama cli -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16
# Run inference directly in the terminal:
llama cli -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16
# Run inference directly in the terminal:
./llama-cli -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16

Use Docker

docker model run hf.co/anyze/Ze1.5-Automotive-Embedded-Instruct:F16

LM Studio
Jan

vLLM

How to use anyze/Ze1.5-Automotive-Embedded-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anyze/Ze1.5-Automotive-Embedded-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anyze/Ze1.5-Automotive-Embedded-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anyze/Ze1.5-Automotive-Embedded-Instruct:F16

SGLang

How to use anyze/Ze1.5-Automotive-Embedded-Instruct 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 "anyze/Ze1.5-Automotive-Embedded-Instruct" \
    --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": "anyze/Ze1.5-Automotive-Embedded-Instruct",
		"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 "anyze/Ze1.5-Automotive-Embedded-Instruct" \
        --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": "anyze/Ze1.5-Automotive-Embedded-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use anyze/Ze1.5-Automotive-Embedded-Instruct with Ollama:
```
ollama run hf.co/anyze/Ze1.5-Automotive-Embedded-Instruct:F16
```

Unsloth Studio

How to use anyze/Ze1.5-Automotive-Embedded-Instruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for anyze/Ze1.5-Automotive-Embedded-Instruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for anyze/Ze1.5-Automotive-Embedded-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for anyze/Ze1.5-Automotive-Embedded-Instruct to start chatting

How to use anyze/Ze1.5-Automotive-Embedded-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "anyze/Ze1.5-Automotive-Embedded-Instruct:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use anyze/Ze1.5-Automotive-Embedded-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default anyze/Ze1.5-Automotive-Embedded-Instruct:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use anyze/Ze1.5-Automotive-Embedded-Instruct with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf anyze/Ze1.5-Automotive-Embedded-Instruct:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "anyze/Ze1.5-Automotive-Embedded-Instruct:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use anyze/Ze1.5-Automotive-Embedded-Instruct with Docker Model Runner:
```
docker model run hf.co/anyze/Ze1.5-Automotive-Embedded-Instruct:F16
```

Lemonade

How to use anyze/Ze1.5-Automotive-Embedded-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull anyze/Ze1.5-Automotive-Embedded-Instruct:F16

Run and chat with the model

lemonade run user.Ze1.5-Automotive-Embedded-Instruct-F16

List all available models

lemonade list

Ze1.5-Automotive-Embedded-Instruct / README.md

anyze

Update README.md

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	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- coding
	- python
	- linux
	- systems-programming
	- embedded-systems
	- automotive
	- communication protocols
	- rtos/register
	- tool-calling
	- agent
	- 128k context
	- C
	- CPP
	- 1.5B
	- Microcontroller
	- STM32
	- CAN
	- Ethernet
	- Autosar
	---

	# Anyze Ze1.5 Instruct (Automotive / Embedded Specialist)

	**~1.54B params · 28 transformer layers (hidden 1536, GQA 12/2, SwiGLU) · 128k context
	(131,072, YaRN) · vocab 151,936 · F16 · Apache-2.0** — full details in [Architecture](#architecture).

	A compact 1.5B-parameter instruction-tuned model specialized for **automotive and
	embedded firmware**: C/C++, MCUs (STM32 & friends), RTOS (FreeRTOS/Zephyr), peripherals
	(UART/SPI/I2C/CAN/LIN), ISRs, UDS/OBD diagnostics, MISRA C, and AUTOSAR (Classic &
	Adaptive) — with agentic tool calling and a verify-by-web-search behavior for
	precise specification values. On top of the specialty it keeps solid general coding
	ability: Python, Linux/systems, and cross-platform shell work (PowerShell,
	cmd, bash). Context window: 128k tokens (YaRN).

	> Scope: a small (1.5B) specialist, not a frontier assistant. Strongest on embedded code,
	> concept explanations, and tool-driven workflows, with everyday Python/Linux/shell as a
	> general baseline. For exact spec values (service IDs, register words, rule numbers,
	> timing limits) it prefers to verify via a search tool rather than answer from memory —
	> serve it with a `web_search` tool available for best factual reliability. Always
	> review generated code before flashing.

	## Capabilities

	- Embedded C/C++: drivers, ISRs, ring buffers, register-level code, RTOS patterns.
	- Automotive: CAN/CAN-FD/LIN mechanics, UDS diagnostics flow, AUTOSAR concepts (RTE,
	SWC, BSW/MCAL, COM, DEM/DCM), MISRA C themes.
	- General coding & shell: everyday Python and Linux/systems tasks, plus
	cross-platform terminal ops (PowerShell, cmd, bash) — installing Python/uv/pip,
	creating venvs, and the agentic "detect platform → install the missing tool → use it"
	pattern.
	- Concept explanations: `volatile`, mutex vs semaphore, priority inversion, DMA,
	watchdogs, memory sections, Cortex-M interrupts/faults.
	- Agentic tool calling (protocol below): emits exactly one tool call and stops;
	routes own-code questions to `grep`/`read`, live facts (versions, prices, errata,
	CVEs) and precise spec values to `web_search`; answers well-known concepts directly.
	- Frontier-style behavior: handles general requests naturally (greetings, everyday
	coding) instead of refusing, and states uncertainty on unverifiable spec figures.
	- Long context: 131,072-token window (YaRN); a 128k KV-cache needs several GB of
	memory — size your hardware accordingly.

	## Example prompts

	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_search`
	- `Where is our CAN receive ISR defined?` → calls `grep`

	## Tool-calling protocol (```json code block)

	The model emits tool calls as a ```json code block and then stops:

	````
	```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}

	# Tools

	You 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 the
	function name and arguments, then stop:
	```json
	{"name": <function-name>, "arguments": <args-json-object>}
	```
	```

	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-sentence
	rationale before each call, plus the verify/restraint policy:

	```text
	When 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.
	```

	## Usage (transformers)

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = 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.

	## Usage (Ollama / LM Studio)

	A ready F16 GGUF is provided. Ollama — create a `Modelfile`:

	```
	FROM ./Ze1.5-1.5B-Automotive-Embedded-Instruct-F16.gguf
	SYSTEM """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 0.7
	PARAMETER top_p 0.8
	PARAMETER top_k 20
	PARAMETER repeat_penalty 1.1
	```
	```bash
	ollama create ze1_5-embedded -f Modelfile
	ollama 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.

	## Limitations

	- Exact spec values: service-ID tables, register reset words, rule numbers and
	timing figures can be confabulated when answered from memory — it prefers to verify
	via `web_search` when the tool is present; run it with a search tool for factual
	work and treat from-memory numbers as unverified.
	- Correct concept explanations are sometimes decorated with imprecise tails; phrasing
	can affect recall. Register-exact code (peripheral bit fields) should be checked
	against the reference manual.
	- Long-context quality beyond ~32k is extrapolated; don't expect book-length attention
	fidelity.
	- English only. 1.5B-scale reasoning: fine for focused tasks, not long multi-step proofs.

	## Architecture

	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.