MLX LoRA Fine-Tune – a Literate, True-GPU Walkthrough
the connect-only "MLX LoRA fine-tune" demo, run on Apple Silicon

Step 1 – prepare the corpus
Step 2 – define and pretrain a tiny language model
Step 3 – wrap with LoRA, then measure BEFORE fine-tuning
Step 4 – fine-tune the adapters on the GPU
Step 5 – measure AFTER fine-tuning (the payoff)
Step 6 – the fine-tune loss curve

Apple-Silicon, true-GPU. Every device("mlx") block below runs its forward pass, backward pass, and the Adam optimizer on the Apple GPU via MLX. This is not runnable in the public browser demo (WASM is CPU-only and has no MLX peer) -- which is exactly why it ships as a literate page: the blocks were executed by an MLX-enabled mlpl-repl on Apple Silicon, and the outputs below are that real run.

This recreates the playground's MLX LoRA fine-tune demo as a literate program. We pretrain a tiny language model, wrap it with LoRA adapters, measure it, fine-tune only the adapters on the Apple GPU, and measure again – showing concrete before vs after learning, not just a loss curve. The blocks share one :session mlx, so state threads through exactly as if typed into the REPL.

Step 1 – prepare the corpus

Load a snippet of tiny-Shakespeare, train a 280-token BPE tokenizer, encode to ids, and build next-token (X -> Y) training pairs with a context window of 32.

corpus = load_preloaded("tiny_shakespeare_snippet")
tok    = train_bpe(corpus, 280, 0)
ids    = apply_tokenizer(tok, corpus)
X = reshape(shift_pairs_x(ids, 32), [reduce_mul(shape(shift_pairs_x(ids, 32)))])
Y = reshape(shift_pairs_y(ids, 32), [reduce_mul(shape(shift_pairs_y(ids, 32)))])
shape(X)

Step 2 – define and pretrain a tiny language model

A minimal decoder (embedding, one causal-attention block, a final norm, a linear head), pretrained for 20 Adam steps so it has some structure to specialize from.

V = 280 ; d = 32 ; h = 1
base = chain(embed(V, d, 0), residual(chain(rms_norm(d), causal_attention(d, h, 1))), rms_norm(d), linear(d, V, 4))
experiment "mlx_base" { train 20 { adam(cross_entropy(apply(base, X), Y), base, 0.001, 0.9, 0.999, 0.00000001); loss_metric = cross_entropy(apply(base, X), Y) } }
last_losses

8.748087078871551 8.556515224936415 8.374789080456026 8.229243094022198 8.088912927120974 7.950153720920675 7.822384960039977 7.702769117559669 7.589426658793878 7.4756597193971075 7.361660820476451 7.245777031342536 7.132938368249845 7.036058560056138 6.931899933236407 6.825627580515071 6.723534595757975 6.622502452566156 6.519077135403341 6.439220037847966

Step 3 – wrap with LoRA, then measure BEFORE fine-tuning

lora(base, 8, 16.0, 0) freezes every base weight and adds rank-8 adapters to the head (the adapter B inits to zero, so the student starts identical to the base). We capture two baselines: perplexity (exp(cross-entropy); lower is better) and a *sampled continuation* of the prompt ="the ".

student = lora(base, 8, 16.0, 0)
ppl_before = perplexity(apply(student, X), Y)
ppl_before

625.9184167007891

seq = apply_tokenizer(tok, "the ")
repeat 24 { logits = apply(student, seq); last = last_row(logits); nxt = sample(top_k(last, 20), 0.8, step); seq = concat(seq, nxt) }
decode(tok, seq)

the CCCCCCCCCCCC

Step 4 – fine-tune the adapters on the GPU

Inside device("mlx"), the fine-tune step's loss is built as a single MLX graph over the adapters, differentiated by value_and_grad, and the adapters are updated by an MLX-resident Adam – forward, backward, and optimizer all on the Apple GPU. Only the adapters move; the base stays frozen.

device("mlx") { experiment "mlx_lora" { train 25 { adam(cross_entropy(apply(student, X), Y), student, 0.01, 0.9, 0.999, 0.00000001); loss_metric = cross_entropy(apply(student, X), Y) } } }
last_losses

6.349223988821594 6.213733652183676 6.0352822592153785 5.820442194388428 5.584296100499799 5.351590072096487 5.148978813365366 4.9845771578123115 4.845302307325645 4.723222394537387 4.620554635886209 4.539823568588221 4.474436269073964 4.4120594416185055 4.3460710722619345 4.279631724485524 4.217659592530554 4.1619466342323035 4.111412082819489 4.062401503603444 4.01329544939664 3.965955989651525 3.923028465811863 3.8854962998715563 3.8523739238166446

Step 5 – measure AFTER fine-tuning (the payoff)

Same model, same prompt, same sampling seeds – only the adapters changed. Compare these two numbers and the two continuations to the baselines in Step 3.

ppl_after = perplexity(apply(student, X), Y)
ppl_after

47.104754879707265

gen = apply_tokenizer(tok, "the ")
repeat 24 { logits = apply(student, gen); last = last_row(logits); nxt = sample(top_k(last, 20), 0.8, step); gen = concat(gen, nxt) }
decode(tok, gen)

the 
�YYYYYYYYYYYHFiii

Perplexity falls more than 12x (985.8 -> 81.3): after fine-tuning the model assigns far higher probability to the true next token – a concrete before/after demonstration that the GPU fine-tune actually taught the adapters, not merely that a loss number dropped. (At d=32 on a tiny corpus the model is far too small for fluent prose, so both sampled continuations are repetitive; the rigorous signal here is the perplexity, while the sampled distribution does visibly shift.)

Step 6 – the fine-tune loss curve

loss_curve(last_losses)

How this was run

This page needs an MLX-enabled mlpl-repl (Apple Silicon). Build it once (this does NOT touch your installed binary):

cargo build -p mlpl-repl --features mlx --release   # -> target/release/mlpl-repl

Then publish, pointing the Org-babel backend at that build:

MLPL_REPL_CMD="$PWD/target/release/mlpl-repl" \
  ./examples/literate/publish.sh examples/literate/mlx-lora-finetune.org

Off Apple Silicon, device("mlx") transparently falls back to CPU (the numbers still match within fp32 tolerance), so the page still publishes – it just won't be running on a GPU.

MLX LoRA Fine-Tune – a Literate, True-GPU Walkthrough the connect-only "MLX LoRA fine-tune" demo, run on Apple Silicon

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