README.md

---
library_name: mlx
license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-230M/blob/main/LICENSE
base_model: LiquidAI/LFM2.5-230M
pipeline_tag: text-generation
language:
  - en
tags:
  - mlx
  - lora
  - tool-calling
  - on-device
  - lfm2.5
  - edge
---

# pomo-1

A LoRA fine-tune of [LiquidAI/LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M)
for **on-device to-do tool-calling**. Given a short natural-language utterance and the
user's current to-do list, `pomo-1` emits a single structured tool call to create,
update, or delete a to-do. Built to run locally on Apple Silicon via MLX.

This is a task-specific model, not a general assistant. It does one thing: turn an
utterance + a small list of existing to-dos into one JSON tool call.

## Intended use

- **In scope:** single-turn to-do CRUD intent → one tool call, on-device.
- **Out of scope:** multi-turn dialogue, reasoning, general chat, code, or any task
  the base model is not recommended for (advanced math, code generation, creative
  writing). Inherits the base model's limits.

## Tools

| tool | arguments |
|---|---|
| `create_todo` | `title` (str), `due` (str \| null) — ignores the current list |
| `update_todo` | `target` (str), `title` (str \| null), `due` (str \| null) |
| `delete_todo` | `target` (str) |
| `none` | `{}` — emitted when the referenced to-do is not in the list |

For `update_todo` / `delete_todo`, `target` is a **verbatim copy** of one item in the
provided list. If the referenced item is absent, the model emits `none`.

## Prompt format

The current to-do list (0–5 items) is injected into the prompt. Training and inference
must use this exact layout:

```
Todos:
- <todo 1>
- <todo 2>

User: <utterance>
```

An empty list renders as `Todos:\n(none)`. Output is a JSON string, e.g.:

```json
{"name":"delete_todo","arguments":{"target":"Book the moving truck"}}
```

## Usage (MLX)

```python
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load("<YOUR_HF_REPO>")  # e.g. sabeshbesh/pomo-1

prompt = "Todos:\n- Book the moving truck\n- Water the front yard plants\n\nUser: delete moving truck task"
tokens = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True, tokenize=True,
)
out = generate(model, tokenizer, prompt=tokens, max_tokens=96,
               sampler=make_sampler(temp=0.0))
print(out)  # {"name":"delete_todo","arguments":{"target":"Book the moving truck"}}
```

Greedy decoding (`temp=0.0`) is recommended for deterministic tool calls. The base
model's general-purpose defaults are temperature 0.1, top_k 50, repetition_penalty 1.05.

## Training

- **Base:** LiquidAI/LFM2.5-230M (LFM2 hybrid: 14 layers, 8 gated short-conv + 6 GQA).
- **Method:** LoRA SFT via `mlx_lm.lora`.
- **Adapter:** rank 16, scale 16, dropout 0.05, applied to all 14 layers; `mask_prompt: true`.
- **Data format:** legacy `{"prompt", "completion"}` JSONL; completion is a JSON-string
  tool call.
- **Hardware:** Apple Silicon (MLX).

<!-- FILL: dataset size, iters, and final checkpoint used for this upload. -->

## Evaluation

<!-- NOTE: numbers below are from the v1 dataset (list NOT in prompt). Replace with the
     list-aware (v2) results before treating these as representative, and report on a
     held-out test split, not the selection val set. -->

Held-out validation (n=300), best checkpoint, greedy decoding:

| metric | score |
|---|---|
| parse rate (valid JSON) | 1.000 |
| tool-name accuracy | 0.997 |
| argument exact-match | 0.563 |
| full match (name + args) | 0.563 |

Metric definitions: **parse rate** = fraction of outputs that are valid tool-call JSON;
**tool-name accuracy** = correct tool selected; **argument exact-match** = arguments
exactly correct; **full match** = both correct.

**Known limitation of this eval:** these figures were measured on a dataset variant
where the current to-do list was *not* included in the prompt, which makes correct
`target` selection for update/delete structurally impossible in many cases — the
argument/full-match scores are a floor, not a ceiling. No baseline comparison against
the prior model is included. Treat these numbers as provisional.

## License

Governed by the base model's license, `lfm1.0`. See the
[base model license](https://huggingface.co/LiquidAI/LFM2.5-230M/blob/main/LICENSE).

## Citation

Base model:

```bibtex
@article{liquidAI2026230M,
  author  = {Liquid AI},
  title   = {LFM2.5-230M: Built to Run Anywhere},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-230m}
}
```