Text Generation
Transformers
Safetensors
GGUF
English
qwen2
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
conversational
text-generation-inference
Instructions to use anyze/Ze1.5-Automotive-Embedded-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - 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 Desktop
- Pi
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 @earendil-works/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
- 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
- Hermes Agent
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
- OpenClaw
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"
File size: 2,797 Bytes
4b01eec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | FROM ./Ze1.5-1.5B-Automotive-Embedded-Instruct-F16.gguf
# Tool-aware ChatML template. Two reasons it must reference .Tools:
# 1) Ollama sets the model's "tools" capability ONLY if the template has a .Tools node — that's
# what makes GitHub Copilot (and other clients) show Tools / agent mode for this model.
# 2) When a client passes tools, they're rendered into the system `# Tools` block in the exact
# format this model expects.
# NOTE: this model emits tool calls as a ```json code block (not a special tool-call token), so the
# tool instruction AND the assistant .ToolCalls rendering use a ```json fence, and tool results
# come back as <tool_response> in a user turn.
TEMPLATE """{{- if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}
# 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>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</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>}
```
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}```json
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}```
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}"""
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
# Copilot/agent clients send large prompts (system + tool defs + file context). Default num_ctx
# 4096 truncates those; 16384 fits a typical agent turn on 8 GB (weights ~3GB + KV ~0.5GB).
PARAMETER num_ctx 16384
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
|