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Model: Shpigford/cron-mini Source: Original Platform
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140
README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: mlx
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- cron
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- systemd
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- devops
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- schedule
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- text-generation
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- mlx
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- lora
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datasets:
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- Shpigford/cron-schedule-conversion
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---
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# Shpigford/cron-mini
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A small fine-tuned language model that converts natural-language schedules into cron expressions and systemd `OnCalendar` strings.
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## What it does
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```
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Input: every Tuesday at 3am except December
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Output: {"cron": "0 3 * 1-11 2",
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"systemd": "Tue *-01..11-* 03:00:00",
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"note": "Months 1-11 only excludes December."}
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```
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It handles:
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- Standard schedules (daily, weekly, monthly, every N minutes/hours)
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- Holidays (Christmas, Thanksgiving, Black Friday, Halloween, etc.)
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- Casual time references ("lunchtime", "before bed", "first thing in the morning")
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- Ordinal weekdays ("second Tuesday of the month", "last Friday")
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- Negative specifications ("every day except Sunday", "all months except December")
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- Sub-minute intervals (cron can't, systemd can — model annotates the limitation)
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- Awkward intervals (every 90 minutes — cron can't, expanded across the day)
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- Compound schedules requiring multiple cron lines
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- systemd-specific features (`OnBootSec=`, `Persistent=`, `RandomizedDelaySec=`)
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- Time zones (sets `TZ=` for cron, uses `Asia/Tokyo`-style for systemd)
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- Typos and informal phrasings ("evry tues @ 3am")
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## Usage
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### MLX (Apple Silicon)
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("Shpigford/cron-mini")
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SYSTEM = ("You convert natural-language schedules into cron expressions and "
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"systemd OnCalendar strings. Output JSON with keys: cron, systemd, "
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"note. If cron cannot exactly express the schedule, put the closest "
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"valid cron and explain in note. Do not output anything else.")
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messages = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": "Convert this schedule to cron and systemd OnCalendar: every weekday at 9am"},
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]
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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print(generate(model, tokenizer, prompt=prompt, max_tokens=200, temp=0.0))
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```
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### Transformers (any platform)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("Shpigford/cron-mini", torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("Shpigford/cron-mini")
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SYSTEM = "..." # same as above
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messages = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": "Convert this schedule to cron and systemd OnCalendar: every weekday at 9am"},
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]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out = model.generate(inputs, max_new_tokens=200, do_sample=False)
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print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
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```
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### llama.cpp / Ollama (GGUF)
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A GGUF version is available — see the Files tab for `.gguf` files. Load with llama.cpp or import into Ollama:
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```bash
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ollama create cron-mini -f Modelfile
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```
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## Evaluation
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Held-out test set of 91 cases including all the trick categories above:
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- **Overall (cron+systemd both correct):** 63/91 (69.2%)
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- **Cron exact match:** 73/91 (80.2%)
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- **Cron syntactically valid:** 87/91 (95.6%)
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- **systemd exact match:** 71/91 (78.0%)
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See `eval_results.json` in this repo for per-case results.
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## Training
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- **Base model:** `Qwen/Qwen2.5-1.5B-Instruct` (Apache 2.0)
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- **Method:** LoRA fine-tune via [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms)
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- **Hardware:** M4 Mac mini, 16GB unified memory
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- **Dataset:** ~3000 examples — hand-crafted hard cases + templated generation + Claude-API paraphrases and synthetic novel cases (verified with a self-check pass)
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- **Dataset on HF:** [Shpigford/cron-schedule-conversion](https://huggingface.co/datasets/Shpigford/cron-schedule-conversion)
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## Limitations
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- The model emits a single best-guess for ambiguous fuzzy times (e.g., "morning" → 7am). It will not ask clarifying questions.
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- For "every other Monday" / "biweekly" / "fortnightly" patterns, cron cannot express them natively — the model emits "every Monday" and notes the limitation. Gate in your script with a week-of-year check.
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- For "last day of month" / "last Friday", cron has no native expression — the model approximates with day-of-month ranges and flags the limitation.
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- Vixie cron OR-matches DOM and DOW when both are restricted; the model emits expressions that work under the more common AND-matching interpretation. Verify on your specific cron implementation.
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- Time zone handling: cron has no built-in TZ field; the model emits the schedule in the system's local time and notes when a `TZ=` env var is needed.
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- Trained on English. Other languages will likely degrade significantly.
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## License
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Apache 2.0, same as the base model.
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## Citation
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If you find this useful:
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```bibtex
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@misc{cron-mini,
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author = {Pigford, Josh},
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title = {Cron-Mini: A Small Model for Schedule Conversion},
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year = {2026},
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howpublished = {Hugging Face},
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url = {https://huggingface.co/Shpigford/cron-mini}
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}
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```
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": [
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151645,
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151643
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],
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"sliding_window": 32768,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.43.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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eval_results.json
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eval_results.json
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example.py
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example.py
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#!/usr/bin/env python3
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"""Minimal inference example for Shpigford/cron-mini."""
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from mlx_lm import load, generate
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model, tokenizer = load("Shpigford/cron-mini")
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SYSTEM = ("You convert natural-language schedules into cron expressions and "
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"systemd OnCalendar strings. Output JSON with keys: cron, systemd, "
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"note. If cron cannot exactly express the schedule, put the closest "
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"valid cron and explain in note. Do not output anything else.")
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def schedule_to_cron(nl: str) -> str:
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messages = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": f"Convert this schedule to cron and systemd OnCalendar: {nl}"},
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]
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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return generate(model, tokenizer, prompt=prompt, max_tokens=200, temp=0.0)
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if __name__ == "__main__":
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import sys
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print(schedule_to_cron(" ".join(sys.argv[1:]) or "every weekday at 9am"))
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generation_config.json
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{
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"bos_token_id": 151643,
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"pad_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
|
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_p": 0.8,
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"top_k": 20,
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"transformers_version": "4.37.0"
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}
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model.safetensors
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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31
tokenizer_config.json
Normal file
31
tokenizer_config.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": true,
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||||
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"tool_parser_type": "json_tools",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user