Show HN: Fine-tune an 8B model on a 4 GB laptop GPU

Soup Fine-tune and post-train LLMs in one command. No SSH, no config hell. Website · Quick Start · Config · Docs · Commands · Models · Discord Soup turns the pain of LLM fine-tuning into a simple workflow. One config, one command, done. pip install "soup-cli[train]" # add [train] to fine-tune; bare `soup-cli` is the light CLI soup init --template chat soup train Why Soup? Training LLMs is still painful. Even experienced teams spend 30-50% of their time fighting infrastructure instead of improving models. Soup fixes that. Zero SSH. Never SSH into a broken GPU box again. One config. A simple YAML file is all you need. Auto everything. Batch size, GPU detection, quantization — handled. Works locally. Train on your own GPU with QLoRA. No cloud required. What's New v0.72.4 — align on a laptop: DPO, ORPO, SimPO and KTO over layer streaming. Layer streaming keeps the frozen base out of VRAM and feeds it to the GPU one decoder layer at a time. It used to support supervised fine-tuning only; now it runs the preference losses too. DPO's reference model is free. DPO needs a reference to compare against, and a second copy of the model would double memory and defeat the whole point. Soup uses the same streamed base with its adapters switched off — one set of weights, one stream. Measured on an RTX 3050 4 GB: streamed DPO peaked at 0.914× the supervised-fine-tuning peak. Forcing a real second model in the same test cost +730 MB — exactly one copy of the weights. KTO is not reference-free, however it is usually described: it picks its reference the same way DPO does, so it gets the same treatment. ORPO and SimPO genuinely are. Bit-exact against a normal, non-streamed run of the same loss — 0.0 difference, the bar every release in this series has to clear. The VRAM pre-flight knows a paired loss is twice the rows, because chosen and rejected go through the model as one tensor. Honest cost: the reference is free in memory, not in time — DPO reads the layer stack 1.52× as often per step as supervised fine-tuning does. grpo / ppo stay excluded on purpose: generation re-reads every layer per token, which is exactly what streaming cannot amortise. Still BETA. # soup.yaml — then just `soup train --config soup.yaml` training: stream_layers: true # base streams out of VRAM; only the adapter trains quantization: 4bit # NF4 — ~4x smaller store, so 8B fits a 4 GB card batch_size: 4 # v0.72.3: bigger batches amortise the weight read stream_source: auto # RAM when it fits, NVMe disk when it does not Trained with stream_layers: true on v0.72.0? That adapter is inert — its tensors were saved under keys with an extra .inner. segment, so every loader returned the untuned base. Fixed in v0.72.1; re-run or re-save. Check with: python -c "from safetensors.torch import load_file; print([k for k in load_file('adapter_model.safetensors') if '.inner.' in k][:3])" Previous release — v0.71.40, soup reward synth (generate a reward verifier from your data) Point soup reward synth at a JSONL of reference outputs and it infers a deterministic verifier, writes a readable / committable .py reward function, and — the part nobody else does — refuses to emit one that can't tell your references from bad answers (four families: numeric / json_schema / regex / tool_call; a mandatory calibration report is the moat). Reward ensembles (reward_fn: "accuracy,format") also train now. (#311) soup reward synth references.jsonl -o reward.py --output-report calib.json Previous release — v0.71.39, CI for weights not prompts (emit + provenance-bind the ship verdict) soup ship's verdict became emittable, committable, and provenance-bound: --emit-evidence makes a run replay into an identical verdict, eval.ship in soup.yaml + --config makes the gate policy reviewable, and --config binds evidence to the exact recipe that produced it (stale evidence → exit 3). soup ship --push owner/repo#N posts the SH