Add Ze1.5 Automotive/Embedded Instruct (F16 safetensors, tokenizer, README, LICENSE)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

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+   Copyright 2024 Alibaba Cloud
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+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
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+   See the License for the specific language governing permissions and
+   limitations under the License.
+---
+Anyze Ze1.5 Instruct (Automotive Embedded Systems)
+Copyright (c) 2026 Anyze
+
+This model includes machine-learning weights derived from Qwen2.5-Coder-1.5B-Instruct
+(Copyright Alibaba Cloud, Apache License 2.0). The original license above is retained.
+The original weights were heavily modified and packaging: Anyze, 2026. Same Apache-2.0 terms.

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
+---
+
+# 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.

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Ze1.5, an embedded-systems and automotive firmware specialist.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Ze1.5, an embedded-systems and automotive firmware specialist.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "rope_scaling": {
+    "rope_type": "yarn",
+    "type": "yarn",
+    "factor": 4.0,
+    "original_max_position_embeddings": 32768
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.44.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.12.1"
+}

model.safetensors

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+oid sha256:539a56efe5953aa0b68a21abb54eef4c79711d4bf9112c5aac4a5ebcc9cd76c3
+size 3087466808

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892

tokenizer_config.json

@@ -0,0 +1,31 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Ze1.5, an embedded-systems and automotive firmware specialist.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Ze1.5, an embedded-systems and automotive firmware specialist.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
+}