gguf-trainer

A trainer GUI for pig_clip adapters: small bridge networks that let pig_clip (native train/fine-tune shipped as a GGUF) stand in for a diffusion model's original text encoder in the ggk engine. Two trainer packs ship:

• LLaDA-Image-Turbo — the 16B LLaDA2-MoE text stack is replaced by pig_clip + a 256-query resampler adapter (the trainer8 recipe); the adapter is text-only, so it pairs unchanged with the model's SigVQ vision encoder for image editing.
• MageFlow-Edit — the Qwen3-VL-4B-Instruct text stack is replaced by pig_clip + a token-aligned adapter with a vision extension (the trainer5 recipe); the adapter pairs with the unchanged mmproj-qwen3vl-4b-it-f16.gguf vision encoder for editing and text-to-image.

Both packs train against pig_clip-f16.gguf as the student.

pip install gguf-trainer
gguf-trainer                # opens http://127.0.0.1:8655/ in the browser

The GUI runs in your browser against a local backend, in the style of the ggk diffuser GUI. Nothing is uploaded: models, datasets and outputs are addressed by filesystem path through the built-in file browser.

What the GUI does

Setup tab — everything a run needs, in one project folder:

• Project: create or open a project, picking its trainer pack. A project is a directory holding the downloaded materials, corpus, precomputed shards, checkpoints and eval.json, plus project.json (settings) and state.json (progress). The exported GGUFs land next to the project folder by default (test-trainer/pig_llada_adapter-f16.gguf for the project test-trainer/llada_adapter/), so every adapter trained under one folder ends up side by side; the Output section can point them elsewhere. The evaluation lives inside the project folder, so several projects sharing one output folder never overwrite each other's eval.json.
• Materials: the pack lists what it needs — the teacher from Hugging Face (LLaDA: inclusionAI/LLaDA-Image-Turbo text encoder, QueryFormer, text_projection, tokenizer, ~33 GB; MageFlow: Qwen/Qwen3-VL-4B-Instruct, ~8.3 GB), the student tokenizer/config (callgg/pig-clip-tokenizer, ~11 MB), pack extras (LLaDA: the optional SigVQ vision encoder, ~2.4 GB; MageFlow: the image dataset(s) selected under Corpus), and your local pig_clip-f16.gguf. One Download missing button fetches everything that is not on disk yet; it is disabled (and so is each material's own button) as soon as the files are present, so a second click can never start a duplicate download. Files you already have are found automatically — in the project, in the directory the GUI was started from, in another project's materials/, or in a folder named by GGUF_TRAINER_MATERIALS — and linked instead of downloaded again (a pig_clip*.gguf next to those is picked as the student). Downloads run as detached processes (they survive closing the GUI) and resume where they stopped after an interruption or a reboot. Headless: gguf-trainer download --project DIR does the same in the foreground.
• Corpus: pick public prompt datasets (Stable Diffusion prompts, Midjourney prompts, DiffusionDB, VidProM) and/or your own .txt/.jsonl files, or point at ready-made train.txt/val.txt. ~1% empty prompts are injected so the adapter learns the empty CFG prompt. Image packs add image datasets (Flickr30k, COCO captions — downloaded as materials) and/or local image folders (optional <name>.txt caption next to each image); edit instructions are synthesized from the captions, and the prompt datasets supply the text-only share on the text-to-image template.
• Precompute / Training / Output: the pack's reference hyper-parameters, editable (LLaDA: width 1024, depth 6, 20k steps, batch 32, lr 2e-4 …; MageFlow: width 1024, depth 4, 20k steps, batch 32), the device, memory budgets for the teacher, the adapter name, output folder and an optional copy destination (your ggk model folder).

Train tab — start/stop/resume the pipeline, a stage strip (corpus → precompute val → precompute train → train → export → eval) with per-stage progress and ETA, live loss / cosine / val-cosine chart, current metrics, and live GPU/CPU/RAM readings of the machine and the pipeline process.

Hardware tab — GPUs (nvidia-smi + torch), RAM, disk, Python/torch/ transformers versions. Logs tab — the pipeline log, following. Output tab — exported files, the evaluation of the exported GGUF, and the ggk command that uses it (Copy).

Resuming after a reboot

The pipeline runs as a detached process (setsid / detached process group) and every stage is idempotent and checkpointed:

• corpus files and each shard are written atomically and skipped when present;
• training saves last.pt every N steps (and on Stop / SIGTERM), including the optimizer, RNG and the exact position in the shard stream;
• export/eval rerun only when the checkpoint is newer than the GGUF. The Export GGUF button (Output tab) or a click on the Export / Evaluate stage boxes regenerates them from best.pt on demand, e.g. after the GGUF was deleted (gguf-trainer start --project DIR --only export eval --force).

Open the project (or start the GUI with gguf-trainer --auto-resume, which relaunches the last project if its process died while running) and press Start / Resume. The same works headless:

gguf-trainer run    --project ~/gguf-trainer/projects/llada_adapter   # foreground
gguf-trainer start  --project ~/gguf-trainer/projects/llada_adapter   # detached
gguf-trainer stop   --project ~/gguf-trainer/projects/llada_adapter   # saves, then exits
gguf-trainer status --project ~/gguf-trainer/projects/llada_adapter
gguf-trainer download --project ~/gguf-trainer/projects/llada_adapter   # fetch missing materials

The LLaDA-Image pack

Teacher target per prompt = the 256 QueryFormer rows of cap_feats ([256, 2560]): LLaDA2-MoE over [tokens ; 256 queries] with the text masked from seeing the queries, then the 6-layer text_projection. QueryFormer and text_projection are re-implemented in plain torch (bit-exact against the diffusers originals) so no diffusers install or reference checkout is needed; the MoE backbone loads through trust_remote_code from the snapshot and is placed sequentially: the chosen GPU up to its budget, then the other CUDA devices, then CPU RAM (the Precompute tab can switch to accelerate's balanced split, which caps the biggest card at an even share of the model and offloads the rest — roughly half the throughput).

Student = pig_clip final-norm hidden states over the engine's exact template (<role>HUMAN</role> Generate an image: {text}\n<role>ASSISTANT</role>\n<IMAGE1>), Qwen BPE without special tokens. The adapter is a seedless Perceiver resampler (self-attn + cross-attn + GELU MLP, head_dim 64) trained with whitened MSE + cosine on per-dim standardized targets; the export folds the standardization into out_proj and writes f16 weights / f32 norms, biases and query, exactly the layout pig_llada_adapter-f16.gguf shipped with.

Use it in ggk (≥ 0.5.7):

ggk diffuser engine -- --diffusion-model LLaDA-image-turbo-nvfp4.gguf \
    --vae pig_flux2_vae_fp32-f16.gguf \
    --llm pig_clip-q8_0.gguf --llm-adapter pig_llada_adapter-f16.gguf \
    --llm_vision pig_llada_sigvq-f16.gguf \
    --ref-image sheep.png -p "a sheep in sunglasses" --cfg-scale 1.0 \
    --steps 4 --sampling-method euler --diffusion-fa -o out.png

Text-to-image works with any student quantization; editing wants pig_clip at q8_0 or better. Judge a run by val centred cosine / rel_mse (0.965 / 0.0024 on the reference 5090 run, ~55 min of training); plain cosine on these rows is ~0.99 even for a zero prediction.

transformers 5 and the teacher's rotary tables. transformers 5 builds models on the meta device and does not re-initialize the non-persistent buffers of remote (trust_remote_code) models, so the LLaDA2-MoE backbone came up with an uninitialized inv_freq RoPE table. The teacher still emitted plausible rows (same mean and scale, plain cosine 0.999 to the real ones) but with the positional signal scrambled, and adapters trained on those targets ignore the prompt in the engine (0.1–0.2 lower centred cosine against the true teacher; edits return the reference image). Since 0.0.3 the teacher repairs the tables after loading and refuses to run with a bad one, and every shard directory carries a CONTRACT marker: shards written before the fix (contract llada_image/1 or none) are discarded on the next run, the checkpoints trained on them are moved to checkpoints.stale-<time>/, and precompute + training start over. A quick health check of any adapter is its centred cosine against a teacher-conditioned engine context (trainer8/dumps/m1_gpu_full/context.bin): ≥ 0.96 is healthy, ~0.87 is the broken-RoPE signature.

The MageFlow-Edit pack

MageFlow-Edit conditions its DiT on Qwen3-VL-4B-Instruct final-norm hidden states with the reference image spliced in as mmproj vision tokens. The pack distills that conditioning into pig_clip + a token-aligned adapter with a vision extension (trainer5): position i of the student maps to position i of the teacher, and because ggk splices the 2560-d mmproj embeds into the LLM input while the 0.6B student embeds at 1024, the adapter owns the bridge in both directions — a frozen vision_proj (2560 → 1024, ridge least squares over the shared vocabulary, applied by the engine to every mmproj embed before the student) and a trained vis_in (2560 → width) that hands the adapter the raw mmproj embeds, so vision fidelity does not depend on what survives the student. The 4B mmproj stays exactly as the teacher uses it; nothing else needs converting.

The engine contract is replicated exactly (verified against ggk SD_DUMP_COND dumps): the 64-token edit template / 34-token text-to-image template, the 6-token Image N: <|vision_start|> header, nearest-neighbour resize to a multiple of 32 with the long side capped at 384, OpenAI-CLIP normalisation, the vision tower's main merger output only (ggk drops deepstack), all-equal M-RoPE (= plain rope), final-norm tap. The vision tower is the one the mmproj was converted from (HF bf16 vs engine f16: cosine 0.998), the text stack matches the engine's q4_k_m teacher at the known quantisation floor (0.975 on text positions).

Corpus (trainer5 mix): 56k single-image + 3k two-image samples with instructions synthesized from the captions (plain and truncated captions, add / remove / replace / restyle / recolor / background patterns, ~1.5% empty) plus 27k text-only prompts; val = 1024 image + 512 text samples, val images never in train. Default image source is Flickr30k (4.4 GB, 31k photos with 5 captions each — several instructions per photo); the COCO captions preset (17 GB, 113k photos) is the trainer5 source. Every sample stores the teacher and student states of every token plus the raw vision embeds (~2.4 MB per image sample, ~200 GB for the full mix) — pick the corpus size for your disk.

Precompute runs the vision tower, the Qwen3-VL text stack and pig_clip once per shard. The text stack (~7.5 GB bf16) stays resident when the GPU budget allows, otherwise it is streamed through the GPU with accelerate's cpu_offload (≈ 1–2 samples/s on a 6 GB laptop card, ~44 on an RTX 5090 resident); batch/token budgets at 0 are chosen per card. Training uses the trainer5 recipe: width 1024 (its width gate winner), depth 4, 20k steps, batch 32, lr 2e-4, warmup 1k, whitened MSE + 0.5·(1−cos) masked to real tokens, out_proj zero-initialised, vision_proj frozen. The export writes the same layout as the shipped pig_qwen3vl_4b_adapter-f16.gguf (f16 weights, f32 norms/biases/vision_proj).

Use it in ggk:

ggk diffuser engine -- --diffusion-model mageflow-edit-turbo-nvfp4.gguf \
    --vae pig_mageflow_vae_fp32-f16.gguf \
    --llm pig_clip-q8_0.gguf --llm-adapter pig_qwen3vl_4b_adapter-f16.gguf \
    --llm_vision mmproj-qwen3vl-4b-it-f16.gguf \
    --ref-image sheep.png -p "a sheep in sunglasses" --cfg-scale 1.0 \
    --steps 4 --sampling-method euler --diffusion-fa -o out.png

Judge a run by cos_slice (cosine over the positions the DiT consumes) and the cos_vis / cos_txt split: the trainer5 reference reached val cos 0.915 (vision positions 0.80, text 0.98) and its A/B edits were near-identical to the teacher's — the 4-step DiT forgives far more than the cosine suggests. A lagging vision cosine means the vis_in path or the width is the limiter, not the student.

Requirements

Python ≥ 3.10, PyTorch (CUDA strongly recommended), transformers, accelerate, safetensors, huggingface_hub, datasets, pyarrow, pillow, gguf-connector, psutil. Running the LLaDA teacher needs ~34 GB of combined GPU + CPU memory; the reference run used an RTX 5090 with CPU offload (4.5 prompts/s, ~3.7 h for 60k prompts). The Qwen3-VL teacher needs ~9 GB of GPU memory resident, or ~9 GB of RAM plus any CUDA card when streamed. Training the adapter itself fits in a few GB of VRAM.

Set GGUF_TRAINER_MOCK_TEACHER=1 (or tick the checkbox under Precompute) to run the whole pipeline with synthetic targets — a smoke test of the machinery, never a usable adapter.

Adding a pack

Subclass gguf_trainer.packs.base.TrainerPack: declare the adapter kind (resampler or token_aligned_vision), the materials (model or dataset snapshots, local files), the pack's config defaults and GUI hints, the prompt template / build_teacher() / build_mock_teacher(), and the export key/values, then register it in gguf_trainer/packs/__init__.py. packs/llada_image.py and packs/qwen3vl_mageflow.py are the two references.