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328ccd1c2e
- harness/evaluate.py: partição congelada com SHA-256, amostra com seed, lote 1, truncamento comum, contagem de tokens, latência (sem aquecimento), pico de RAM e GPU; teste bloqueado até o congelamento da configuração. - analysis/metrics.py: acurácia, macro F1, Brier, NLL, ECE, acurácia seletiva. - Diagnósticos por resposta (metadata.diagnostics); Laya informa opções colapsadas pelo orçamento de cabeçalho; head_max_len via DMB_LAYA_HEAD_MAX_LEN. - infra/ds/collect.py e gpu_queue --env para pilotos e rodadas. - .gitignore: de models/ só metadados (cards, configs, licenças, índices); pesos, tokenizadores grandes, runtimes, fontes clonados e caches ignorados. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
167 lines
9.1 KiB
Markdown
167 lines
9.1 KiB
Markdown
---
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license: apache-2.0
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base_model:
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- XHToken/Spark-X2.5-4B
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- XHToken/Spark-X2.5-1.7B
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library_name: peft
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tags:
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- gguf
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- lora
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- classification
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- typed-decisions
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- rizzo-flow
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language:
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- en
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---
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# Rizzo Flow: Spark-X2.5 (4B and 1.7B) + typed-decisions LoRA
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These are LoRA fine-tunes of [XHToken/Spark-X2.5-4B](https://huggingface.co/XHToken/Spark-X2.5-4B)
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(recommended) and [XHToken/Spark-X2.5-1.7B](https://huggingface.co/XHToken/Spark-X2.5-1.7B), for [Rizzo Flow](https://github.com/Rizzo-AI-Academy/rizzo-flow). Rizzo Flow turns an unstructured
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state into typed decisions (`boolean`, `choice`, `score`) with probabilities, and it **generates no
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text**. The model runs one forward pass per question. The answer is the softmax over the logits of
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the answer letters (`A`, `B`, …) at the last prompt position.
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Training works the same way. There is no text target: the loss is a soft cross-entropy between the
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target distribution and the softmax over the answer letters. Only the LoRA adapters are trained.
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This model is not affiliated with TypeSafe, and it is not Jev.
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## Files
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| File | What it is |
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| --- | --- |
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| `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 |
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| `spark-x2.5-4b-rizzo-flow-lora-q8_0.gguf` | Merged model, Q8_0 (4.4 GB): the default for `rizzo serve` |
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| `spark-x2.5-4b-rizzo-flow-lora-bf16.gguf` | Merged model, BF16 (8.2 GB) |
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| `spark-x2.5-4b-rizzo-flow-lora-q4_k_m.gguf` | Merged model, Q4_K_M (2.6 GB), for GPUs with little memory |
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| `lora/` | The PEFT adapter alone (r 16, alpha 32), to apply to the BF16 HF checkpoint |
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| `training/history.json` | Arguments, training loss and dev evaluations of the 4B run |
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| `spark-x2.5-1.7b-rizzo-flow-lora-q8_0.gguf` | 1.7B merged model, Q8_0 (1.8 GB) |
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| `spark-x2.5-1.7b-rizzo-flow-lora-bf16.gguf` | 1.7B merged model, BF16 (3.4 GB) |
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| `lora-1.7b/` | The 1.7B PEFT adapter alone (r 16, alpha 32) |
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| `training/history-1.7b.json` | The same for the 1.7B run |
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## Use
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These GGUF files are what [Rizzo Flow](https://github.com/Rizzo-AI-Academy/rizzo-flow) downloads
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by default. It pins this repository by commit and checks the sha256 of every file:
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```bash
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git clone https://github.com/Rizzo-AI-Academy/rizzo-flow && cd rizzo-flow
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uv sync --locked
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uv run rizzo download # llama.cpp for this machine + this model, Q8_0 (4.4 GB)
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uv run rizzo serve # → http://127.0.0.1:8017/playground
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```
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`--quant bf16` or `--quant q4_k_m` picks another file, `--size 1.7b` the small model (also in its own repository,
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[`rizzoaiacademy/rizzo-flow-1.7b`](https://huggingface.co/rizzoaiacademy/rizzo-flow-1.7b)), and
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`--weights base` the original Spark-X2.5 GGUF files. The server answers as `rizzo-flow-4b-q8_0`.
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**MLX** (Apple Silicon, or MLX-CUDA) uses the safetensors checkpoint at the root of this
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repository instead of the GGUF files:
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```bash
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uv sync --locked --extra mlx
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uv run --no-sync rizzo download --backend mlx # the safetensors only, 8.2 GB
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uv run --no-sync rizzo serve --backend mlx --bits 8
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```
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It answers as `rizzo-flow-4b-bf16` (or `…-q8`/`…-q4` with `--bits`). On an RTX 5060 Ti with MLX-CUDA, BF16, the
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4B gives the same prompts (same hashes) and the same probabilities as llama.cpp BF16 within 0.001.
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**transformers** loads the checkpoint with the original Spark code (`trust_remote_code=True`,
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`modeling_spark.py` is XHToken's file, unchanged): `AutoModelForCausalLM.from_pretrained(
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"rizzoaiacademy/rizzo-flow", trust_remote_code=True, dtype="bfloat16")`. Use the prompts of
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Rizzo Flow and read the answer-letter logits at the last position; generating text is not what
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it was trained for.
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The GGUF files are plain GGUF (architecture `spark2_5`; Rizzo Flow runs them on llama.cpp release b11081), but the model is
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meant to be **read, not chatted with**: Rizzo Flow builds the prompt, then reads the logits of the
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answer letters. In a chat app it behaves like a lightly changed Spark-X2.5, and the fine-tune
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brings nothing there.
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The adapter was trained on the exact prompts of `PROMPT_VERSION = "spark-decisions-v3"`, commit
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`c30cc63` of rizzo-flow. Any other prompt format can make the adapter useless.
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## Results
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We ran the test split of the
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[`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) benchmark
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(config `all`, 400 cases, 2,000 decisions). The model is fine-tuned for typed decisions, so this
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is not a zero-shot result, but training used no data from these four workflows: the Open-Jev config
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that shares them (`workflow-controls-v1`) was excluded, and so was every training state containing
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text from the test set. Base and fine-tune were measured on the same machine: llama.cpp b11081,
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CUDA, RTX 5060 Ti.
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| | Accuracy ↑ | KL from gold ↓ | Brier ↓ | ECE ↓ | p50 per case |
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| --- | ---: | ---: | ---: | ---: | ---: |
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| Spark-X2.5-4B Q8_0, base | 0.574 | 2.899 | 0.480 | 0.349 | 201 ms |
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| **This model, Q8_0** | **0.648** | **0.452** | **0.205** | **0.112** | 195 ms |
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| TypeSafe Jev 1.13.0 (from its dataset card, not re-measured) | 0.727 | 1.442 | 0.148 | – | – |
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Accuracy +0.074, 95% interval [+0.050, +0.101] (paired bootstrap over cases). On an RTX PRO 6000
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the same file scores 0.650 / 0.454 / 0.205 / 0.111: the difference between GPUs is below 0.002.
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**Q4_K_M** (made with `llama-quantize` b11081 from the BF16 GGUF, no importance matrix) scores
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0.650 / 0.436 / 0.201 / 0.093 on the RTX 5060 Ti, p50 198 ms: tied with Q8_0 on this benchmark
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(+0.002 [−0.011, +0.015]). On the original weights Q4_K_M cost 4 points on SemIf's fixtures, which
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we have not re-run here, so check it on your own data before relying on it.
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Per workflow (accuracy / KL): customer_service 0.710 / 0.350, invoice_processing 0.714 / 0.506,
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security_incidents 0.674 / 0.434, agent_trace_observability 0.500 / 0.526 (base: 0.366).
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By type: noul 0.738, choice 0.637, score 0.593.
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Dev split of the training mixture (600 questions): loss 1.928 → 0.393, accuracy 0.547 → 0.857.
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### 1.7B
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We measured the base model and the fine-tuned one on the same machine, with the same runtime
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(RTX PRO 6000; on the RTX 5060 Ti: 0.530 → 0.544, KL 3.031 → 0.694):
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| | Accuracy ↑ | KL from gold ↓ | Brier ↓ | ECE ↓ | p50 per case |
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| --- | ---: | ---: | ---: | ---: | ---: |
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| Spark-X2.5-1.7B Q8_0, base | 0.531 | 3.032 | 0.494 | 0.348 | 50 ms |
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| **This model, 1.7B Q8_0** | **0.546** | **0.692** | **0.274** | **0.167** | 52 ms |
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On the 1.7B, fine-tuning mostly improves the probabilities (KL, Brier, ECE). Accuracy barely
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moves: +0.015 is within noise. The change is not uniform across workflows:
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agent_trace_observability goes 0.372 → 0.490 and customer_service 0.644 → 0.674, but
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security_incidents drops 0.608 → 0.520. By type, noul goes 0.603 → 0.675 and score 0.441 → 0.461,
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but choice drops 0.578 → 0.532. On the dev split, loss goes 3.117 → 0.460 and accuracy
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0.408 → 0.820. Use the 4B when accuracy matters.
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The probabilities are **not calibrated**. Fit temperature scaling on data from your own domain
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(`rizzo calibrate`) before you use thresholds. The accuracy is still below Jev's published
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number, and the gold labels of the benchmark come from a ~4B teacher model, not from humans. We
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have not yet re-run the smoke and SemIf fixtures on this model.
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## Training
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- Base: Spark-X2.5-4B BF16, rev. `0bcb356`. LoRA r 16, alpha 32, dropout 0.05, on all linear
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layers of attention and MLP. Embedding and `lm_head` are not trained because they are tied.
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32.4M trainable parameters.
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- AdamW, lr 5e-5, 3% warmup then cosine decay, no weight decay, clip 1.0. 16 questions per step.
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1 epoch = 1,770 steps. Seed 0.
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- 28,321 training questions (14.7M prompt tokens, max 2,048 tokens per prompt), from
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[`tasksource/procedural-typed-decisions`](https://huggingface.co/datasets/tasksource/procedural-typed-decisions)
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(Apache-2.0), [`ZefanCai/Open-Jev`](https://huggingface.co/datasets/ZefanCai/Open-Jev)
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`release-v2-redistributable` (CC0) and 12 configs of
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[`Praveenrajus/jev-bench`](https://huggingface.co/datasets/Praveenrajus/jev-bench). None of the
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labels come from Jev or TypeSafe. We removed every training state that contains text from the
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evaluation sets.
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- Hardware: 1× RTX PRO 6000 Blackwell 96 GB, no gradient checkpointing. 4B: batch 4 / 2,048
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tokens per forward, ~6,700 prompt tokens/s, 41 minutes, 38 GiB peak. 1.7B (rev. `14d6e83`,
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16.7M trainable parameters, same data and hyperparameters): batch 8 / 3,072 tokens,
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~15,500 tokens/s, 19 minutes, 22 GiB peak.
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Full pipeline and reasoning:
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[`docs/training.md`](https://github.com/Rizzo-AI-Academy/rizzo-flow/blob/main/docs/training.md).
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## Licenses of the training data
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The base model is Apache-2.0. Among the jev-bench configs, `stsb` is **CC-BY-SA**.
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`measuring_hate_speech` and the HelpSteer2 configs are CC-BY-4.0: *Measuring Hate Speech*
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(Kennedy et al., 2020; Sachdeva et al., 2022) and *HelpSteer2* (Wang et al., 2024, NVIDIA).
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Check whether these terms matter for your use before you redistribute derivatives.
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