---
license: mit
library_name: pytorch
tags: [tool-calling, agent, tiny-llm, byte-level, on-device, from-scratch]
pipeline_tag: text-generation
---

# ultra-tiny-1m — LocalAgent (0.98M params)

A **from-scratch, byte-level** tool-calling agent model from
[LocalAgent](https://github.com/sangbumchoi/localagent). Pure PyTorch, **0.98M params**,
trained on CPU. It pairs a tiny decoder (GQA + RoPE + SwiGLU + depth-recurrence) with a **dual head**
(tool-selection classifier + pointer/copy argument head) and **prompt-grounded constrained
decoding** for reliable tool calls across 21 tools (general assistant, the Claude Code /
Codex coding surface, and computer-use / productivity tools), including parallel two-call turns.

## Architecture
- vocab 256 (byte-level), d_model 192, layers 2 x6 loops, heads 6/2 (GQA), ffn 640
- factorized embeddings: True

## Files
- `config.json` — `ModelConfig`
- `model.safetensors` / `pytorch_model.bin` — decoder weights
- `agent_heads.bin` — trained tool-selection + pointer heads (optional)

## What it can do (use cases)
One byte-level model that turns a natural-language turn into a grounded tool call — across an
assistant, a coding agent, computer-use/productivity apps, and **parallel two-call** turns:

| you say | it calls |
|---|---|
| "What's the weather in Cusco?" | `get_weather(city="Cusco")` |
| "What is 19 * 19 * 5?" | `calculator(expression="19*19*5")` |
| "Open the file bin/run.sh." | `read_file(path="bin/run.sh")` |
| "Grep for 'TODO'." | `grep_search(pattern="TODO")` |
| "Run the tests." | `run_tests()` |
| "Commit with message 'fix bug'." | `git_commit(message="fix bug")` |
| "Send an email to Greta." | `send_email(recipient="Greta")` |
| "Go to figma.com." | `open_url(url="figma.com")` |
| "Send a Slack message saying 'ship it'." | `slack_send(message="ship it")` |
| "Create a Jira ticket titled 'broken link'." | `jira_issue(summary="broken link")` |
| "Compose an email to Judy **and** search for how tall is Everest." | `send_email(recipient="Judy")` + `web_search(query="how tall is Everest")` |

Multi-turn coding (grounds a follow-up arg from a tool response):
`read_file(tests/test_api.py)` → result → `run_tests()` → "FAILED…" → fix.
At catalog scale (100s–1000s of tools) selection is done by **retrieval** (top-k) instead of a
fixed head. See the [LocalAgent repo](https://github.com/sangbumchoi/localagent).

## Load (pure PyTorch, no transformers)
```python
import json, torch
from huggingface_hub import hf_hub_download
from localagent.model import LocalAgentLM, ModelConfig

cfg_d = json.load(open(hf_hub_download("danelcsb/localagent-ultra-tiny-1m", "config.json")))
cfg = ModelConfig(**{k: v for k, v in cfg_d.items() if k in ModelConfig.__dataclass_fields__})
model = LocalAgentLM(cfg)
from safetensors.torch import load_file
model.load_state_dict(load_file(hf_hub_download("danelcsb/localagent-ultra-tiny-1m", "model.safetensors")))
model.eval()
```
See the LocalAgent repo for the grounded decoder / agent runtime (tool head, pointer head,
retrieval, parallel-call decode).