MakazhanAlpamys/Soup

Fine-tune and post-train LLMs in one command. No SSH, no config hell.

Soup turns the pain of LLM fine-tuning into a simple workflow. One config, one command, done.

Fine-tune an 8B model on a 4 GB laptop GPU. Layer streaming keeps the frozen base out of VRAM and feeds it to the GPU one decoder layer at a time. Measured on an RTX 3050 Laptop 4 GB: Llama-3.1-8B-Instruct + NF4 at 119.6 tok/s, 3.32 GB peak — bit-exact against a normal resident run, and reproduced independently on an H100 at 113.00 tok/s in the same 3.32 GB. (The tok/s figure was measured on v0.72.2, before the v0.73.0 correctness repair that cost −4.8% at 32B; it has not been re-run on a 4 GB card since.) Opt-in (stream_layers: true) and still BETA — how it works · all measurements · paper · check it yourself on a free Colab T4 (caps the process to 4 GB, then asserts a streamed model is bit-identical to a normal one)

Llama-3.1-8B-Instruct + NF4, LoRA, batch 1, seq 512 on an RTX 3050 Laptop 4 GB — 3.32 GB peak, 119.6 tok/s. Full video (90s)

Training LLMs is still painful. Even experienced teams spend 30-50% of their time fighting infrastructure instead of improving models. Soup fixes that.