Neurolume

Watch your model learn. Fine-tune an LLM on your Mac in a few clicks — and see, layer by layer, what training actually changed.

▶ Try the online demo (no install; replays a real recorded run) · English · 简体中文

Most fine-tuning tools stop at "training finished, loss = 1.23". Most interpretability tools show a model you cannot change. Neurolume puts both in one local app: you train the model, and you watch its insides while you do it.

Status: early alpha. Server, UI and tooling are unit-tested (110+ tests, no GPU needed). Everything below runs on one machine so far (MacBook Pro M5 Max, 128 GB). Verified there with Devstral Small 2 and Qwen3.6-27B: live layer probe, logit lens (live, and computed afterwards for answers generated without it), per-token confidence, guided LoRA training (Devstral: 36 steps, 15 GB peak, validation loss 4.45 → 1.06), before/after comparison, MLX export and training-data generation. Ollama export is not hardware-tested yet. Reports from other machines are very welcome.

What you can do

Fine-tune without writing code

• Bring data, or make it. Save good answers (or your corrected versions) from the chat with one click; drop in .jsonl, notes, code, or your ChatGPT / Claude / Claude Code chat history — or let the loaded model generate a dataset from your own files: Q&A pairs from documents, or "requests → your own code/writing" pairs that teach your style. You review and edit every item before training.
• Guided LoRA training with automatic checks (secret scan, de-duplication, train/validation split), live loss, speed and memory. The base model is never modified; the result is a separate adapter.
• Did it work? Validation loss before → after on data the model never trained on, a per-layer bar chart of how much each layer changed, and a side-by-side comparison: the same question answered by the original and the trained model (no reload — the adapter is switched off and on in place).
• Take it anywhere. Export the fused model as MLX (LM Studio loads it directly) or to Ollama.

See inside the model

• Live brain view. A 3D sphere or nebula with one band per real layer (Devstral Small 2: 40; Qwen3.6-27B: 64 with its linear/full-attention pattern). It discharges only while the model is generating or training.
• Layer map. Four weight matrices per layer with real weight values on hover, per-layer activity while generating, and the adapter's real ΔW while training.
• Logit lens. For each generated token, what every layer would predict "if the model stopped here" — watch the answer form layer by layer. Generated an answer with the view closed? Open it afterwards and it is computed for that answer in one extra pass, so normal chatting stays at full speed.
• Answer replay. One bar per token showing how confident the model was; the answer text is tinted the same way and hovering shows the alternatives it considered. Click any token and the brain and layer views go back to that moment.
• "How to read this" explanations for attention, MLP, linear attention, LoRA and the logit lens, in English and Chinese.

Use it every day

• Chat with an OpenAI-compatible local server (/v1/chat/completions, streaming), one-click model library, knowledge base (import books/docs for retrieval), opt-in web search for local models (also as an MCP server), and an agent that reads a project folder and proposes edits you approve one by one (with undo).

Honest signals

Everything labelled as the model's activity comes from the model; simulated data is labelled. Full definitions in docs/SIGNALS.md.

Shown	Source
Layer count and types	Read from the loaded model
Weight values on hover	Exact strided sample of the dequantized weights
Per-layer activity	mean|out − in| / mean|in| of each block at the token being generated; raw value on hover
Logit lens	The model's own final norm + output head applied to intermediate layers
Per-token confidence and alternatives	The probabilities mlx-lm already computes while generating (no extra pass)
Brightness of the links between layers	Geometric mean of the two layers' activity; which matrix each thin line joins is illustrative
ΔW, per-layer change	Computed from the adapter (scale · B·A)
Validation loss before → after	mlx-lm's evaluation before the first update and at the end
Discharge paths on the sphere, colours, glow	Stylized (their frequency follows GPU/memory load and layer activity)

Tour of the interface

The window is split in two: chat on the right, and on the left the brain on top, with the layer window and the answer replay below it.

1. Chat (right)

• First row: pick a model; green “Live · real model” means the data comes from the running model, yellow “demo” means simulated. After training you also get an adapter dropdown (“no adapter” is the base model) and a Compare with base button.
• Toolbar: Models (download, load) · Train (the wizard) · Web search (the gear opens search settings) · Knowledge base · 中文/English · Agent · 🗑 clear chat.

This is the beginning of the README. Read the rest at the original. Read the full README ↗