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Model: Shpigford/cron-mini
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+  - en
+library_name: mlx
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+  - cron
+  - systemd
+  - devops
+  - schedule
+  - text-generation
+  - mlx
+  - lora
+datasets:
+  - Shpigford/cron-schedule-conversion
+---
+
+# Shpigford/cron-mini
+
+A small fine-tuned language model that converts natural-language schedules into cron expressions and systemd `OnCalendar` strings.
+
+## What it does
+
+```
+Input:  every Tuesday at 3am except December
+
+Output: {"cron": "0 3 * 1-11 2",
+         "systemd": "Tue *-01..11-* 03:00:00",
+         "note": "Months 1-11 only excludes December."}
+```
+
+It handles:
+
+- Standard schedules (daily, weekly, monthly, every N minutes/hours)
+- Holidays (Christmas, Thanksgiving, Black Friday, Halloween, etc.)
+- Casual time references ("lunchtime", "before bed", "first thing in the morning")
+- Ordinal weekdays ("second Tuesday of the month", "last Friday")
+- Negative specifications ("every day except Sunday", "all months except December")
+- Sub-minute intervals (cron can't, systemd can — model annotates the limitation)
+- Awkward intervals (every 90 minutes — cron can't, expanded across the day)
+- Compound schedules requiring multiple cron lines
+- systemd-specific features (`OnBootSec=`, `Persistent=`, `RandomizedDelaySec=`)
+- Time zones (sets `TZ=` for cron, uses `Asia/Tokyo`-style for systemd)
+- Typos and informal phrasings ("evry tues @ 3am")
+
+## Usage
+
+### MLX (Apple Silicon)
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("Shpigford/cron-mini")
+
+SYSTEM = ("You convert natural-language schedules into cron expressions and "
+          "systemd OnCalendar strings. Output JSON with keys: cron, systemd, "
+          "note. If cron cannot exactly express the schedule, put the closest "
+          "valid cron and explain in note. Do not output anything else.")
+
+messages = [
+    {"role": "system", "content": SYSTEM},
+    {"role": "user", "content": "Convert this schedule to cron and systemd OnCalendar: every weekday at 9am"},
+]
+prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+print(generate(model, tokenizer, prompt=prompt, max_tokens=200, temp=0.0))
+```
+
+### Transformers (any platform)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("Shpigford/cron-mini", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("Shpigford/cron-mini")
+
+SYSTEM = "..."  # same as above
+messages = [
+    {"role": "system", "content": SYSTEM},
+    {"role": "user", "content": "Convert this schedule to cron and systemd OnCalendar: every weekday at 9am"},
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=200, do_sample=False)
+print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+### llama.cpp / Ollama (GGUF)
+
+A GGUF version is available — see the Files tab for `.gguf` files. Load with llama.cpp or import into Ollama:
+
+```bash
+ollama create cron-mini -f Modelfile
+```
+
+## Evaluation
+
+Held-out test set of 91 cases including all the trick categories above:
+
+- **Overall (cron+systemd both correct):** 63/91 (69.2%)
+- **Cron exact match:** 73/91 (80.2%)
+- **Cron syntactically valid:** 87/91 (95.6%)
+- **systemd exact match:** 71/91 (78.0%)
+
+See `eval_results.json` in this repo for per-case results.
+
+## Training
+
+- **Base model:** `Qwen/Qwen2.5-1.5B-Instruct` (Apache 2.0)
+- **Method:** LoRA fine-tune via [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms)
+- **Hardware:** M4 Mac mini, 16GB unified memory
+- **Dataset:** ~3000 examples — hand-crafted hard cases + templated generation + Claude-API paraphrases and synthetic novel cases (verified with a self-check pass)
+- **Dataset on HF:** [Shpigford/cron-schedule-conversion](https://huggingface.co/datasets/Shpigford/cron-schedule-conversion)
+
+## Limitations
+
+- The model emits a single best-guess for ambiguous fuzzy times (e.g., "morning" → 7am). It will not ask clarifying questions.
+- 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.
+- For "last day of month" / "last Friday", cron has no native expression — the model approximates with day-of-month ranges and flags the limitation.
+- 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.
+- 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.
+- Trained on English. Other languages will likely degrade significantly.
+
+## License
+
+Apache 2.0, same as the base model.
+
+## Citation
+
+If you find this useful:
+
+```bibtex
+@misc{cron-mini,
+  author = {Pigford, Josh},
+  title = {Cron-Mini: A Small Model for Schedule Conversion},
+  year = {2026},
+  howpublished = {Hugging Face},
+  url = {https://huggingface.co/Shpigford/cron-mini}
+}
+```

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- 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 Qwen, created by Alibaba Cloud. You are a helpful assistant.<|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,
+        151643
+    ],
+    "hidden_act": "silu",
+    "hidden_size": 1536,
+    "initializer_range": 0.02,
+    "intermediate_size": 8960,
+    "max_position_embeddings": 32768,
+    "max_window_layers": 21,
+    "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,
+    "sliding_window": 32768,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.43.1",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

example.py

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+#!/usr/bin/env python3
+"""Minimal inference example for Shpigford/cron-mini."""
+from mlx_lm import load, generate
+
+model, tokenizer = load("Shpigford/cron-mini")
+
+SYSTEM = ("You convert natural-language schedules into cron expressions and "
+          "systemd OnCalendar strings. Output JSON with keys: cron, systemd, "
+          "note. If cron cannot exactly express the schedule, put the closest "
+          "valid cron and explain in note. Do not output anything else.")
+
+def schedule_to_cron(nl: str) -> str:
+    messages = [
+        {"role": "system", "content": SYSTEM},
+        {"role": "user", "content": f"Convert this schedule to cron and systemd OnCalendar: {nl}"},
+    ]
+    prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+    return generate(model, tokenizer, prompt=prompt, max_tokens=200, temp=0.0)
+
+if __name__ == "__main__":
+    import sys
+    print(schedule_to_cron(" ".join(sys.argv[1:]) or "every weekday at 9am"))

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20,
+  "transformers_version": "4.37.0"
+}

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