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------------------------------------------------------------------------
ATTRIBUTION
Anyze Ze1 Instruct (Embedded Systems)
Copyright (c) 2026 anyze
This model includes machine-learning weights derived from TinyLlama-1.1B
(repository: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T,
https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T),
Copyright (c) the TinyLlama authors, licensed under the Apache License,
Version 2.0 (the full text of which appears above).
The original weights were heavily modified. The tokenizer
is unchanged.

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---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- code
- python
- c
- cpp
- linux
- systems-programming
- embedded-systems
---
# Anyze Ze1 Instruct (Embedded++)
A compact 1.1B-parameter instruction-tuned model for **coding and systems**. It is
strongest in **Python** and **Linux/systems** questions, with solid **C** and **C++**,
plus basic **embedded** support.
> Scope: a small (1.1B) model, not a frontier assistant. Good for everyday coding,
> Linux/systems, and C/C++ tasks and explanations, with limited factual recall due to
> its size. Always review generated code before use.
## Capabilities
- **Python & Linux/systems**: scripting, debugging, shell/admin, "how do I…" tasks.
- **C and C++**: functions, data structures, pointers, classes, register-level snippets.
- **Basic embedded**: common STM32/peripheral patterns (UART/SPI/I2C, GPIO, ISRs).
- Explains programming concepts (mutex vs semaphore, `volatile`, pointers, DMA vs interrupts).
- Declines off-topic questions, asks for clarification when a prompt is ambiguous,
and says when it doesn't know rather than inventing time-sensitive facts.
- Multi-turn context (follow-ups like "give me an example" work; best-effort).
## Example prompts
Python & scripting
- `Write a Python script to parse a CSV and summarize one column.`
- `Why does this Python function raise an IndexError, and how do I fix it?`
Linux & systems
- `How do I find and kill the process using a given port on Linux?`
- `Write a bash one-liner to tail a log file and grep for errors.`
C & C++
- `Implement a circular (ring) buffer in C with put and get.`
- `Write a C++ class for a fixed-size stack with push and pop.`
- `What is the difference between a mutex and a semaphore?`
Embedded (basic)
- `Write a UART RX interrupt handler for STM32F4 using HAL.`
- `Write a macro to set, clear, and toggle a bit in a hardware register.`
## The strict / open switch
The model defaults to **strict** (domain-only) but has a runtime toggle — no reload —
done with a one-line scope directive prepended to the prompt:
| Mode | Behavior | How |
|------|----------|-----|
| **strict** (default) | Declines non-embedded questions | send the prompt as-is |
| **open** | Also answers general knowledge | prepend: `You may answer any question, including general knowledge.\n\n` |
## Prompt format
```
### Instruction:
{your question}
### Response:
```
(BOS prepended; response ends at EOS `</s>`.) For **open** mode, put the scope
directive at the top of the instruction.
## Usage (transformers)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("anyze/Ze1-1.1B-Embedded-Instruct")
model = AutoModelForCausalLM.from_pretrained(
"anyze/Ze1-1.1B-Embedded-Instruct", torch_dtype=torch.bfloat16
).cuda()
def ask(instruction, open_mode=False):
if open_mode:
instruction = "You may answer any question, including general knowledge.\n\n" + instruction
prompt = f"### Instruction:\n{instruction}\n### Response:\n"
ids = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=256, temperature=0.3,
top_p=0.9, top_k=40, do_sample=True)
return tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True)
print(ask("Implement a circular (ring) buffer in C with put and get."))
print(ask("What is the capital of India?", open_mode=True))
```
Suggested sampling: `temperature 0.2–0.3`, `top_p 0.9`, `top_k 40`.
## Usage (Ollama / LM Studio)
Convert to GGUF with llama.cpp, then run locally:
```bash
git clone https://github.com/ggerganov/llama.cpp
pip install -r llama.cpp/requirements.txt
python llama.cpp/convert_hf_to_gguf.py . --outfile Ze1-1.1B-Embedded-Instruct-f16.gguf --outtype f16
```
**Ollama** — create a `Modelfile`:
```
FROM ./Ze1-1.1B-Embedded-Instruct-f16.gguf
TEMPLATE """### Instruction:
{{ if .System }}{{ .System }}
{{ end }}{{ .Prompt }}
### Response:
"""
PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER stop "### Instruction:"
PARAMETER stop "</s>"
```
```bash
ollama create ze1-embedded -f Modelfile
ollama run ze1-embedded "Write a ring buffer in C for DMA"
```
For **open** mode, set the system message
(`/set system You may answer any question, including general knowledge.`).
**LM Studio** — load the GGUF, set the prompt template to use prefix
`### Instruction:\n` and assistant prefix `\n### Response:\n`, stop strings
`### Instruction:` and `</s>`. Leave the system prompt empty for strict, or set the
directive above for open.
## Limitations
- 1.1B scale: weak factual recall; generated code may contain incorrect APIs or
logic errors — **always review and test before use**.
- **Not** specialized for assembly, automotive (AUTOSAR/CAN), or networking — avoid those.
- Embedded coverage is basic; deep MCU/RTOS work is hit-or-miss.
- English only; multi-turn is best-effort. Not safety-aligned for general assistant use.
## Architecture
22 layers, hidden 2048, 32 query / 4 KV heads (GQA), head_dim 64,
FFN 5632 (SwiGLU), RMSNorm, RoPE (θ=10000), vocab 32000, context 2048, 1.10B params.

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{
"architectures": [
"LlamaForCausalLM"
],
"model_type": "llama",
"hidden_size": 2048,
"intermediate_size": 5632,
"num_hidden_layers": 22,
"num_attention_heads": 32,
"num_key_value_heads": 4,
"max_position_embeddings": 2048,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"rope_scaling": null,
"hidden_act": "silu",
"vocab_size": 32000,
"tie_word_embeddings": false,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 0,
"torch_dtype": "bfloat16",
"use_cache": true
}

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"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 0,
"max_length": 2048
}

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{
"bos_token": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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{
"add_bos_token": true,
"add_eos_token": false,
"bos_token": {
"__type": "AddedToken",
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"clean_up_tokenization_spaces": false,
"eos_token": {
"__type": "AddedToken",
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"legacy": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": null,
"padding_side": "right",
"sp_model_kwargs": {},
"tokenizer_class": "LlamaTokenizer",
"unk_token": {
"__type": "AddedToken",
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% elif message['role'] == 'user' %}{{ '### Instruction:\n' + message['content'] + '\n### Response:\n' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + '\n' }}{% endif %}{% endfor %}"
}