skylar 0.2.1

Skylar — local, sovereign, from-scratch LLMs. CLI + loader for the Skylar model family: generative (chat/cobol) and embeddings.

skylar

A tiny runtime + CLI for the Skylar model family — local, sovereign, from-scratch LLMs you load, run, and serve with one pip install. It covers generative chat, embeddings / retrieval, and a COBOL code specialist — all 390M-class or smaller, runnable on a single GPU or CPU, no data leaving your machine.

Models live under Sophia-AI on HuggingFace: Skylar-236M-Base · Skylar-236M-Chat · Skylar-236M-Embed · SkylarCobol-390M.

Install

pip install skylar
# optional HTTP server:
pip install "skylar[serve]"

Use it — CLI

# chat with any Skylar generative model (no forced persona — steer it with --system)
skylar chat --model Sophia-AI/Skylar-236M-Chat --system "Sei un assistente che risponde dal contesto."

# embeddings / retrieval (any SkylarEmbedder model)
skylar embed --model Sophia-AI/Skylar-236M-Embed --query "prestito casa" --docs "mutuo" "meteo"

# one-shot generation (HF repo id or a local checkpoint dir)
skylar generate --model Sophia-AI/Skylar-236M-Chat --prompt "..."

# the COBOL specialist: complete a stub into a full, compilable program
skylar cobol --example --model Sophia-AI/SkylarCobol-390M

# OpenAI-compatible server (needs the [serve] extra) — auto-detects generative vs embedding
skylar serve --model <any-skylar-model> --port 8000
#   generative model -> POST /generate            POST /v1/chat/completions
#   embedder model   -> POST /v1/embeddings

Decoding is greedy by default (--temperature 0.0); there is no forced system prompt — pass --system "..." to steer a chat model. (The skylar cobol subcommand handles the COBOL prompt format for you.)

Use it — Python

import skylar

# generative chat — pass your own system prompt (no forced persona)
m = skylar.load("Sophia-AI/Skylar-236M-Chat")          # HF repo id or a local dir
print(m.generate("Domanda: dove ha sede la Banca d'Italia?",
                 system="Rispondi solo dal contesto fornito."))
for delta in m.stream("..."):                          # streaming
    print(delta, end="", flush=True)

# embeddings / retrieval
e = skylar.load_embedder("Sophia-AI/Skylar-236M-Embed")
ranked = e.rank("costo del denaro", ["la BCE alza i tassi", "ricetta pizza"])

# the COBOL specialist
c = skylar.load("Sophia-AI/SkylarCobol-390M")
print(c.complete_cobol(my_stub))                       # -> full, compilable COBOL program

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/Skylar-236M-Chat")

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).