| --- |
| base_model: LiquidAI/LFM3-1.7B |
| license: other |
| license_name: lfm1.0 |
| license_link: https://huggingface.co/LiquidAI/LFM3-1.7B/blob/main/LICENSE |
| library_name: gguf |
| pipeline_tag: text-generation |
| tags: [gguf, lora, flowchart, mermaid, diagram] |
| --- |
| |
| # termaid |
|
|
| This is the diagram model behind [termaid](https://github.com/WorldOccupier/termaid), a Mac |
| menu-bar app. You type or say what a process does in plain English, and termaid draws it as a |
| Mermaid flowchart. The model runs entirely on your Mac. |
|
|
| It's [LiquidAI/LFM3-1.7B](https://huggingface.co/LiquidAI/LFM3-1.7B) with a LoRA adapter merged |
| in, quantized to Q6_K. The file is 1.43 GB. |
| |
| ## What it does |
| |
| termaid feeds the model one sentence at a time, along with the diagram so far. The model sends back |
| the updated diagram as compact JSON, a list of `boxes` and a list of edges written as |
| `[from, to, label]`. It never writes Mermaid. termaid checks the JSON and builds the Mermaid itself, |
| so a bad reply can't produce broken syntax. A condition like "if the tests fail" becomes a decision |
| box with yes and no branches. |
| |
| Don't expect it to chat. I trained it only on this per-sentence JSON format, and it won't write |
| Mermaid if you ask it to. |
| |
| ## Files |
| |
| | File | Quant | Size | |
| |---|---|---| |
| | `termaid.gguf` | Q6_K | 1.43 GB | |
|
|
| I ship Q6_K because Q4_K_M lost too much. On 55 holdout descriptions with v10, Q6_K matched the |
| unquantized model's diagram 27 times and Q4_K_M only 17 times. Q4_K_M was about 15% faster, which |
| isn't worth it. |
|
|
| ## Usage |
|
|
| The simplest route is the termaid app. The setup screen downloads this file and the app runs it with |
| llama.cpp. |
|
|
| To run it yourself, use LFM3's chat template with an empty `<think></think>` block and temperature |
| 0. That matches how I trained it, with thinking turned off. Here's an Ollama Modelfile. |
|
|
| ``` |
| FROM ./termaid.gguf |
| TEMPLATE """{{- if .System }}<|im_start|>system |
| {{ .System }}<|im_end|> |
| {{ end }} |
| {{- range .Messages }} |
| {{- if eq .Role "user" }}<|im_start|>user |
| {{ .Content }}<|im_end|> |
| {{ else if eq .Role "assistant" }}<|im_start|>assistant |
| <think></think>{{ .Content }}<|im_end|> |
| {{ end }} |
| {{- end }}<|im_start|>assistant |
| <think></think>""" |
| PARAMETER stop "<|im_end|>" |
| PARAMETER temperature 0 |
| ``` |
|
|
| You'll also need the system prompt and the per-sentence message format. Both live in the termaid |
| repo under `src/`. |
|
|
| ## Training |
|
|
| I trained a rank-16 LoRA on LFM3-1.7B on Modal, merged it into the base model, and converted the |
| result to GGUF. The run took 628 steps at an effective batch size of 16, about 1.33 epochs. A |
| learning rate of 1e-4 diverged. |
|
|
| The data has 7,550 training steps and 565 validation steps. Each step is one sentence, the |
| diagram before it, and the diagram after it. They come from three places. |
|
|
| - Template stories using vocabulary from software, hiring, healthcare, logistics, finance and |
| kitchens. |
| - About 300 long stories that Claude Sonnet wrote so that every sentence continues the flow. |
| - About 300 stories aimed at two mistakes v10 kept making. It sent outcomes to the wrong box, and |
| it hung decisions off the wrong step. |
|
|
| Any step whose target diagram failed termaid's own structure checks got dropped. |
|
|
| ## Evaluation |
|
|
| None of these descriptions appear in the training data. |
|
|
| The 55 regular holdouts include domains the model never saw, such as education, legal and |
| manufacturing. |
|
|
| | | v10 | v11 | |
| |---|---|---| |
| | Valid diagrams | 55 | 55 | |
| | Clean, no structure problems | 42 | 41 | |
| | Decisions with both outcomes | 45/59 | 46/61 | |
|
|
| The 40 hard descriptions are long and dense. Claude Sonnet graded each diagram against its |
| description. |
|
|
| | | v10 | v11 | |
| |---|---|---| |
| | Correct, 0 major errors | 1 | 2 | |
| | At most 2 major errors | 7 | 15 | |
| | Total major errors | 152 | 137 | |
| | Wrong target | 43 | 35 | |
|
|
| Sonnet graded each diagram once, so treat a difference of 5 errors or so as noise. The drop in |
| wrong targets is bigger than that, and so is the jump from 7 to 15 nearly-right diagrams. |
|
|
| ## Limitations |
|
|
| The model still struggles with long sentences that pack in several steps. It often handles the |
| first half and drops the rest, so you lose an "otherwise" branch or the flow stops early. More |
| training data barely moved this between v10 and v11. Now and then it adds an edge or box the text |
| never mentioned. I've only trained and tested it on English. |
|
|
| ## License |
|
|
| The weights derive from LFM3-1.7B, so the |
| [LFM Open License v1.0](https://huggingface.co/LiquidAI/LFM3-1.7B/blob/main/LICENSE) covers them. |
| That license excludes commercial use by organizations with $10M or more in annual revenue. The |
| termaid app code is MIT. |
|
|
