smeagle-4b — a small agentic helper you can run at home

It works methodically: read before you write, verify before you claim, report honestly. Small, focused, and unreasonably effective at the work it was trained for.

Why it's worth your VRAM

• −26.9% loss on the assistant tokens it was trained to produce (masked held-out cross-entropy: base 0.568 → smeagle 0.415). That's a large, real shift on the objective that matters.
• 5 / 5 on an agentic task benchmark, zero tool-protocol failures — build-a-plugin (writes + syntax-checks
  • reads back valid Python), multi-tool chaining, file read/modify/write, multi-step calc with verification, and tool-based word counting. Scored by task completion on a real harness, not by proxy loss.

What smeagle can do for you

• Uses tools correctly — picks the right tool, fills the arguments, no coaxing (native function-calling).
• Chains steps — computes a value, writes it to a file, reads it back, uses it in the next step; holds the thread across a multi-step plan.
• Works with files — creates, reads, modifies, and lists files; builds multi-file layouts.
• Writes and verifies code — generates Python functions/modules and syntax-checks them before claiming done, the way a careful engineer would.
• Recovers from errors — when a tool errors (a syntax bug, a bad path), it reads the message and fixes it instead of looping or giving up.
• Extends its own runtime — it can write a working plugin for an agent harness (it built one for the very harness it was benchmarked in).
• Respects constraints — when you need exact output, it gives you exact output, no chatter.

Try this — getting started

• Code: Write a Python function is_prime(n), then walk me through why it's correct for 17 and 18.
• Terminal: I want to find every .log file over 10MB and delete it. Give me the one-liner and explain each part.
• Reason: A file has 12,000 lines and I need to process it in chunks of 500 without loading it all into memory. Sketch the approach.

• List the files in this project, read the main entry point, and tell me what it does.
• Create three files a.txt, b.txt, c.txt — each containing its own name — then list them to confirm.
• Write a factorial(n) function to factorial.py and syntax-check it before you tell me it's done.

Choose your quant (does it run on YOUR machine?)

Run it with llama.cpp

Honest limits — where it actually breaks

• Byte-exact / multi-line formatting. Told to write four exact lines with trailing spaces and no final newline, it collapsed them onto one line. It normalizes formatting — if you need bytes exactly, verify.
• Operand-order-sensitive code. In a generated RPN evaluator it got the subtract/divide order wrong (a op b vs b op a) — the classic bug. Review order-sensitive logic it writes.
• Long single-file running state. Overwriting one file with a running value across many steps, it loses the thread (0/3 in our reruns). Give it distinct filenames or fewer hops when precision matters.
• Temperature-sensitive on one-shot precision. Some exact/self-referential tasks it gets right most of the time but not every time (e.g. "write a sentence stating its own word count" — ~2 in 3 at default temp 0.6). If you need it reliable in one shot, lower the temperature or let it retry.

• It's 4B — a frontier model beats it on hard long-horizon work; smeagle's job is to be the capable little one that fits, not to out-muscle models 100× its size. And it's a specialist (agentic/terminal/ SWE), not a generalist oracle.
• Why v0.1 and not v0.2? We built a v0.2 and benchmarked both, in the open, on all 26 tasks with reruns. v0.2's apparent edge turned out to be temperature noise once repeated — no reliable agentic gain — and it regressed slightly on held-out SWE loss. So v0.1 stays the recommended release. That's the honest call, and we'll only ship a v0.2 when it clearly earns it.

License & rules — free for almost everyone

• Free to use, run, modify, quantize, and ship products with — commercially or not — for individuals, hobbyists, startups, research, schools, and small-to-mid-size companies. Run it on your laptop; build on it.
• Attribution always — keep a visible "Hob Forge — smeagle" credit when you redistribute it.
• The billboard clause — if you're a frontier AI lab or serve smeagle to 10M+ monthly users, you must publicly disclose that your product uses a Hob Forge fine-tune. If it helps you serve millions, say so.
• Guardrails (non-negotiable): no child-sexualizing/endangering use; don't abliterate-and-relabel as Hob Forge; don't pass its output off as human-authored to deceive.

Provenance