rizzoaiacademy/rizzo-flow

Rizzo Flow: Spark-X2.5 (4B and 1.7B) + typed-decisions LoRA

These are LoRA fine-tunes of XHToken/Spark-X2.5-4B (recommended) and XHToken/Spark-X2.5-1.7B, for Rizzo Flow. Rizzo Flow turns an unstructured state into typed decisions (boolean, choice, score) with probabilities, and it generates no text. The model runs one forward pass per question. The answer is the softmax over the logits of the answer letters (A, B, …) at the last prompt position.

Training works the same way. There is no text target: the loss is a soft cross-entropy between the target distribution and the softmax over the answer letters. Only the LoRA adapters are trained.

This model is not affiliated with TypeSafe, and it is not Jev.

Files

File	What it is
model-0000*-of-00005.safetensors, config.json, tokenizer files	Merged model, BF16 safetensors (8.2 GB): for the MLX backend and transformers. Same weights as the BF16 GGUF, bit for bit
spark-x2.5-4b-rizzo-flow-lora-q8_0.gguf	Merged model, Q8_0 (4.4 GB): the default for rizzo serve
spark-x2.5-4b-rizzo-flow-lora-bf16.gguf	Merged model, BF16 (8.2 GB)
spark-x2.5-4b-rizzo-flow-lora-q4_k_m.gguf	Merged model, Q4_K_M (2.6 GB), for GPUs with little memory
lora/	The PEFT adapter alone (r 16, alpha 32), to apply to the BF16 HF checkpoint
training/history.json	Arguments, training loss and dev evaluations of the 4B run
spark-x2.5-1.7b-rizzo-flow-lora-q8_0.gguf	1.7B merged model, Q8_0 (1.8 GB)
spark-x2.5-1.7b-rizzo-flow-lora-bf16.gguf	1.7B merged model, BF16 (3.4 GB)
lora-1.7b/	The 1.7B PEFT adapter alone (r 16, alpha 32)
training/history-1.7b.json	The same for the 1.7B run

Use

These GGUF files are what Rizzo Flow downloads by default. It pins this repository by commit and checks the sha256 of every file:

git clone https://github.com/Rizzo-AI-Academy/rizzo-flow && cd rizzo-flow
uv sync --locked
uv run rizzo download          # llama.cpp for this machine + this model, Q8_0 (4.4 GB)
uv run rizzo serve             # → http://127.0.0.1:8017/playground

--quant bf16 or --quant q4_k_m picks another file, --size 1.7b the small model (also in its own repository, rizzoaiacademy/rizzo-flow-1.7b), and --weights base the original Spark-X2.5 GGUF files. The server answers as rizzo-flow-4b-q8_0.

MLX (Apple Silicon, or MLX-CUDA) uses the safetensors checkpoint at the root of this repository instead of the GGUF files:

uv sync --locked --extra mlx
uv run --no-sync rizzo download --backend mlx      # the safetensors only, 8.2 GB
uv run --no-sync rizzo serve --backend mlx --bits 8

It answers as rizzo-flow-4b-bf16 (or …-q8/…-q4 with --bits). On an RTX 5060 Ti with MLX-CUDA, BF16, the 4B gives the same prompts (same hashes) and the same probabilities as llama.cpp BF16 within 0.001.

transformers loads the checkpoint with the original Spark code (trust_remote_code=True, modeling_spark.py is XHToken's file, unchanged): AutoModelForCausalLM.from_pretrained( "rizzoaiacademy/rizzo-flow", trust_remote_code=True, dtype="bfloat16"). Use the prompts of Rizzo Flow and read the answer-letter logits at the last position; generating text is not what it was trained for.

The GGUF files are plain GGUF (architecture spark2_5; Rizzo Flow runs them on llama.cpp release b11081), but the model is meant to be read, not chatted with: Rizzo Flow builds the prompt, then reads the logits of the answer letters. In a chat app it behaves like a lightly changed Spark-X2.5, and the fine-tune brings nothing there.

The adapter was trained on the exact prompts of PROMPT_VERSION = "spark-decisions-v3", commit c30cc63 of rizzo-flow. Any other prompt format can make the adapter useless.

Results

We ran the test split of the LocalLLaMA/typed-decisions benchmark (config all, 400 cases, 2,000 decisions). The model is fine-tuned for typed decisions, so this is not a zero-shot result, but training used no data from these four workflows: the Open-Jev config that shares them (workflow-controls-v1) was excluded, and so was every training state containing text from the test set. Base and fine-tune were measured on the same machine: llama.cpp b11081, CUDA, RTX 5060 Ti.

	Accuracy ↑	KL from gold ↓	Brier ↓	ECE ↓	p50 per case
Spark-X2.5-4B Q8_0, base	0.574	2.899	0.480	0.349	201 ms
This model, Q8_0	0.648	0.452	0.205	0.112	195 ms
TypeSafe Jev 1.13.0 (from its dataset card, not re-measured)	0.727	1.442	0.148	–	–

Accuracy +0.074, 95% interval [+0.050, +0.101] (paired bootstrap over cases). On an RTX PRO 6000 the same file scores 0.650 / 0.454 / 0.205 / 0.111: the difference between GPUs is below 0.002.

Q4_K_M (made with llama-quantize b11081 from the BF16 GGUF, no importance matrix) scores 0.650 / 0.436 / 0.201 / 0.093 on the RTX 5060 Ti, p50 198 ms: tied with Q8_0 on this benchmark (+0.002 [−0.011, +0.015]). On the original weights Q4_K_M cost 4 points on SemIf's fixtures, which we have not re-run here, so check it on your own data before relying on it.

Per workflow (accuracy / KL): customer_service 0.710 / 0.350, invoice_processing 0.714 / 0.506, security_incidents 0.674 / 0.434, agent_trace_observability 0.500 / 0.526 (base: 0.366). By type: noul 0.738, choice 0.637, score 0.593.

Dev split of the training mixture (600 questions): loss 1.928 → 0.393, accuracy 0.547 → 0.857.

1.7B

We measured the base model and the fine-tuned one on the same machine, with the same runtime (RTX PRO 6000; on the RTX 5060 Ti: 0.530 → 0.544, KL 3.031 → 0.694):

	Accuracy ↑	KL from gold ↓	Brier ↓	ECE ↓	p50 per case
Spark-X2.5-1.7B Q8_0, base	0.531	3.032	0.494	0.348	50 ms
This model, 1.7B Q8_0	0.546	0.692	0.274	0.167	52 ms

On the 1.7B, fine-tuning mostly improves the probabilities (KL, Brier, ECE). Accuracy barely moves: +0.015 is within noise. The change is not uniform across workflows: agent_trace_observability goes 0.372 → 0.490 and customer_service 0.644 → 0.674, but security_incidents drops 0.608 → 0.520. By type, noul goes 0.603 → 0.675 and score 0.441 → 0.461, but choice drops 0.578 → 0.532. On the dev split, loss goes 3.117 → 0.460 and accuracy 0.408 → 0.820. Use the 4B when accuracy matters.

The probabilities are not calibrated. Fit temperature scaling on data from your own domain (rizzo calibrate) before you use thresholds. The accuracy is still below Jev's published number, and the gold labels of the benchmark come from a ~4B teacher model, not from humans. We have not yet re-run the smoke and SemIf fixtures on this model.

Training

• Base: Spark-X2.5-4B BF16, rev. 0bcb356. LoRA r 16, alpha 32, dropout 0.05, on all linear layers of attention and MLP. Embedding and lm_head are not trained because they are tied. 32.4M trainable parameters.
• AdamW, lr 5e-5, 3% warmup then cosine decay, no weight decay, clip 1.0. 16 questions per step. 1 epoch = 1,770 steps. Seed 0.
• 28,321 training questions (14.7M prompt tokens, max 2,048 tokens per prompt), from tasksource/procedural-typed-decisions (Apache-2.0), ZefanCai/Open-Jev release-v2-redistributable (CC0) and 12 configs of Praveenrajus/jev-bench. None of the labels come from Jev or TypeSafe. We removed every training state that contains text from the evaluation sets.
• Hardware: 1× RTX PRO 6000 Blackwell 96 GB, no gradient checkpointing. 4B: batch 4 / 2,048 tokens per forward, ~6,700 prompt tokens/s, 41 minutes, 38 GiB peak. 1.7B (rev. 14d6e83, 16.7M trainable parameters, same data and hyperparameters): batch 8 / 3,072 tokens, ~15,500 tokens/s, 19 minutes, 22 GiB peak.

Full pipeline and reasoning: docs/training.md.

Licenses of the training data

The base model is Apache-2.0. Among the jev-bench configs, stsb is CC-BY-SA. measuring_hate_speech and the HelpSteer2 configs are CC-BY-4.0: Measuring Hate Speech (Kennedy et al., 2020; Sachdeva et al., 2022) and HelpSteer2 (Wang et al., 2024, NVIDIA). Check whether these terms matter for your use before you redistribute derivatives.