pillardash/milogs-action-engine

Milogs Action Engine

A fine-tuned version of Needle — a 26M-parameter Simple Attention Network — specialized for routing natural language voice transcriptions to structured journal, finance, planning, and search actions in the Milogs personal knowledge workspace.

The whole process was orchestrated using DeepSeek V4 Flash on Opencode, and this report too was crated using it.

Base model: Cactus-Compute/needle
Paper: Simple Attention Networks
Original authors: Henry Ndubuaku, Jakub Mroz, Karen Mosoyan, Roman Shemet, Parkirat Sandhu, Satyajit Kumar, Noah Cylich, Justin H. Lee

What it does

Milogs Action Engine converts transcribed speech into structured tool calls. A user says:

"Spent 3500 naira on lunch at that new place"

And the model outputs:

[{"name":"log_transaction","arguments":{"type":"expense","amount":3500,"currency":"NGN","description":"lunch at new place","category":"food"}}]

It handles 7 tools:

Tool	Purpose
log_activity	Time-bounded activity journal entry
log_transaction	Financial transaction (expense/income/transfer)
log_gratitude	Gratitude journal entry
create_plan	Task or plan creation
create_journal_entry	Free-form journal note
search_content	Full-text search across content
add_to_plan	Add notes to existing plans

Architecture

Property	Value
Parameters	26M
Architecture	Encoder-decoder, pure attention (no FFN)
Encoder	12 layers, GQA (8H/4KV), RoPE, gated residuals
Decoder	8 layers, self-attn + cross-attn, gated residuals
d_model	512
Vocab	8192 (SentencePiece BPE)
Norm	ZCRMSNorm (zero-centred, init=0)
Precision	bfloat16
File size	~50 MB

Training

Base Model

The base Needle model was:

• Pretrained on 200B tokens using 16x TPU v6e (27 hours)
• Post-trained on 2B tokens of single-shot function call data (45 minutes)

Fine-tuning (this model)

Detail	Value
Dataset	3,210 synthetic examples (450 per tool + 60 multi-tool)
Generated by	Large language model (via structured prompt)
Train / Val / Test	3,070 / 70 / 70 (per-tool stratified split)
Epochs	2
Batch size	64
Optimiser	AdamW (non-kernel) + Muon (Dense kernels)
Learning rate	Adam: 3e-5, Muon: 0.02
Schedule	WSD (Warmup-Stable-Decay): 4 warmup / 86 stable / 4 decay steps
Loss weighting	Base: 1.0, Name: 2.0, Value: 4.0, Key: 1.5
Hardware	Apple M1 Pro (16 GB), CPU (JAX bfloat16)
Duration	~3.5 hours
Date	2026-07-21

Results

Metric	Base model	Fine-tuned
Name F1	69.2%	100%
Call F1	20.0%	98.6%
Exact match	18.6%	98.6%
Parse rate	100%	100%
Args accuracy	28.9%	98.6%

Per-tool (fine-tuned, test set):

• add_to_plan: 10/10
• create_journal_entry: 10/10
• create_plan: 10/10
• log_activity: 10/10
• log_gratitude: 10/10
• log_transaction: 10/11
• search_content: 10/10

Usage

from needle import SimpleAttentionNetwork, load_checkpoint, generate, get_tokenizer

params, config = load_checkpoint("milogs_action_engine.pkl")
model = SimpleAttentionNetwork(config)
tokenizer = get_tokenizer()

tools = '[{"name":"log_transaction","description":"Record a financial transaction.","parameters":{"type":{"type":"string","required":true},"amount":{"type":"number","required":true}}},{"name":"log_activity","description":"Log an activity.","parameters":{"text":{"type":"string","required":true}}},{"name":"log_gratitude","description":"Log gratitude.","parameters":{"text":{"type":"string","required":true}}},{"name":"create_plan","description":"Create a task.","parameters":{"title":{"type":"string","required":true}}},{"name":"create_journal_entry","description":"Create a note.","parameters":{"text":{"type":"string","required":true}}},{"name":"search_content","description":"Search content.","parameters":{"query":{"type":"string","required":true}}},{"name":"add_to_plan","description":"Add notes to a plan.","parameters":{"plan_title":{"type":"string","required":true},"note":{"type":"string","required":true}}}]'

result = generate(model, params, tokenizer, query="Spent 3500 on lunch", tools=tools, stream=False)
print(result)
# [{"name":"log_transaction","arguments":{"type":"expense","amount":3500,"description":"lunch"}}]

Attribution

This model is a fine-tuned derivative of Needle by Cactus Compute.

@misc{ndubuaku2026needle,
  title={Needle},
  author={Henry Ndubuaku and Jakub Mroz and Karen Mosoyan and Roman Shemet and Parkirat Sandhu and Satyajit Kumar and Noah Cylich and Justin H. Lee},
  year={2026},
  url={https://github.com/cactus-compute/needle}
}

@misc{milogs2026actionengine,
  title={Milogs Action Engine},
  author={Milogs Team},
  year={2026},
  url={https://milogs.app}
}

License

MIT (same as base Needle model)