Skylar — local, sovereign, from-scratch LLMs. CLI + loader for the Skylar model family (COBOL specialist & more).
Project description
skylar
Local, sovereign, from-scratch LLMs — a tiny runtime + CLI for the Skylar model family.
First release ships the COBOL specialist (Sophia-AI/SkylarCobol-390M): a 390M model that
beats 7B general code models on COBOL generation, small enough to run on a single GPU (or CPU).
Install
pip install skylar
# optional HTTP server:
pip install "skylar[serve]"
Use it — CLI
# interactive chat (auto-downloads the model from HuggingFace the first time)
skylar chat
# one-shot completion
skylar generate --prompt "Scrivi un sottoprogramma COBOL che somma due interi in RESULT."
# point at a different model or a local checkpoint
skylar chat --model Sophia-AI/SkylarCobol-390M
skylar generate --model ./my-checkpoint --prompt "..."
# OpenAI-compatible server (needs the [serve] extra)
skylar serve --port 8000
# POST /generate {"prompt": "..."}
# POST /v1/chat/completions {"messages": [...]}
By default the system prompt is "Sei un esperto programmatore COBOL." and decoding is greedy
(--temperature 0.0). Override with --system / --temperature.
Use it — Python
import skylar
m = skylar.load("Sophia-AI/SkylarCobol-390M") # or a local dir
print(m.generate("Complete a COBOL paragraph that stores the max of two numbers in RESULT."))
for delta in m.stream("..."): # streaming
print(delta, end="", flush=True)
skylar also registers the architecture with 🤗 Transformers, so this works too:
import skylar # registers nano-transformer
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Sophia-AI/SkylarCobol-390M")
What's inside
The Skylar models use a custom decoder (NanoTransformer, Qwen3-style: RMSNorm + RoPE + GQA +
QK-Norm + SwiGLU), trained 100% from scratch (no third-party pretrained weights). This package
vendors the architecture so the published weights load anywhere — no private framework needed.
License
Apache-2.0. Models & code IP: A. Ivanovitch (Sophia AI).
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