basal-1.5-mini

basal-1.5-mini (1.5B) is the fastest model of the family, distilled from basal-1.5-max: high-volume routing, laptops and small GPUs. Part of the basal-1.5 family of typed-decision models for Polish and English.

What it is. Inspired by System 1 (fast, intuitive) decision models such as Jev: instead of writing an answer, the model reads a state (a message, a document, retrieved passages, an agent trace, a web page as JSON) and answers a typed question about it (choice, yes/no noul, score, and in the basal engine multi and act) by returning a calibrated probability for each allowed answer, in one forward pass, without generating text. The answer never falls outside the options you give, and the probability says how sure the model is.

What it is for: a dynamic classifier. The classes are described in the request, in plain language, so one model serves many tasks without retraining: routing with changing categories, rule and policy checks, questions about long documents, RAG decisions (relevance, sufficiency, conflicts, claim support, "cannot be determined"), urgency and risk scores, judging answers, agent and tool decisions, and triage with a confidence threshold.

Each model also comes as -MLX-8bit and -MLX-fp4 (Apple Silicon) and -GGUF (Ollama, llama.cpp). NVIDIA ModelOpt checkpoints for vLLM: -FP8 and -NVFP4.

Results

Werdykt (our hidden Polish–English benchmark of typed decisions: 10 categories × 2 languages, one protocol for every model, open models on one H100; macro accuracy over the 20 cells): 0.607 (Polish 0.619, English 0.594), median latency 14.2 ms per decision with the basal engine (fast mode), $0.054 per 1,000 decisions. Leaderboard and public samples: https://basal.si5.pl/.

All served with the basal engine on one H100 (--mode fast, bf16), both option orders averaged. Definitions: README.

Against basal-1.0-1.5B, fp32 readouts of the release checkpoints: the gains are on the decision tasks (public decision tasks, Polish and English, 0.839 vs 0.707; Polish decisions dev 0.901 vs 0.869); on the held-out test mini is level (0.832 vs 0.829, not significant; basal-1.5 0.875, basal-1.5-max 0.893); Polish general knowledge test 0.671 vs 0.658; English external panel 0.581, level with basal-1.0-1.5B's 0.584. Decided automatically: 46.5% at the 1% target (0.90% observed error) and 70.4% at the 5% target, where the observed error, 5.3%, is above the target.

On Werdykt, mini scores 0.607: +8.4 points over basal-1.0-1.5B (0.522) at the same speed and cost, but well below basal-1.5 and basal-1.5-max. Choose mini for speed (14.2 ms per decision on one H100), cost ($0.054 per 1,000 decisions) and memory, on constrained hardware, not for absolute accuracy.

Quick start

1. Install into a fresh uv environment (no git needed):

uv venv --python 3.12 ~/basal-env && source ~/basal-env/bin/activate
uv pip install torch==2.11.0 --index-url https://download.pytorch.org/whl/cu128
uv pip install "basal[fp8] @ https://github.com/rkinas/basal/archive/refs/tags/v1.5.0.tar.gz"

Install torch first, from the CUDA 12.8 index: the newest torch on PyPI may need a newer GPU driver, and the torchvision preinstalled on cloud GPU images breaks transformers. DGX Spark and B300: use the cu130 build (hardware notes).

2. Start the server and wait until it prints basal: model ... ready:

basal-serve --model Remek/basal-1.5-mini --mode fast --port 8000

The first start in mode fast compiles the model for a few minutes; --mode fast-nocompile starts in seconds and --mode fp8 is faster on workstation, consumer and desktop GPUs. vLLM and SGLang: --mode vllm / --mode sglang in separate environments (vLLM: basal[vllm]; SGLang: install sglang[srt]==0.5.21 first, then basal with --no-deps, as in the README). Apple Silicon: basal-1.5-mini-MLX-8bit; Ollama: basal-1.5-mini-GGUF. FP8 checkpoint for vLLM: basal-1.5-mini-FP8.

3. Ask questions (in a second terminal). Several questions about one state go into one request and are answered over one shared state:

curl -s localhost:8000/v1/systemone -H 'content-type: application/json' -d '{
  "state": "Klient: od wczoraj nie mogę zalogować się do bankowości internetowej, system pokazuje błąd hasła.",
  "questions": {
    "dept":   {"type": "choice", "instructions": "Do którego działu skierować zgłoszenie?",
               "criteria": {"cards": "Reklamacje kart", "online": "Wsparcie bankowości elektronicznej", "loans": "Kredyty"}},
    "urgent": {"type": "noul", "instructions": "Czy klient nie może korzystać z usługi?"}}}'

The response has a calibrated probability for every option, the chosen key and the confidence:

{"model": "basal-1.5-mini",
 "answers": {"dept": {"type": "choice", "choice": "online", "probabilities": {"cards": …, "online": …, "loans": …}, "confidence": …},
             "urgent": {"type": "noul", "noul": …, "probabilities": {"true": …, "false": …}, "confidence": …}},
 "usage": {"input_tokens": …, "output_tokens": 0, "questions": 2, "branches": 2, "latency_ms": …}}

From Python (the basal package includes a client):

from basal.client import Basal
b = Basal("http://127.0.0.1:8000")
state = "Klient: od wczoraj nie mogę zalogować się do bankowości internetowej."
a = b.choice(state, "Do którego działu skierować zgłoszenie?",
             {"cards": "Reklamacje kart", "online": "Wsparcie bankowości elektronicznej", "loans": "Kredyty"})
print(a["choice"], a["confidence"])                                      # chosen key and its probability
print(b.yes_no(state, "Czy klient zgłasza problem techniczny?")["noul"])  # P(yes)
print(b.score(state, "Jak pilne jest zgłoszenie?", ["niska", "średnia", "wysoka"])["score"])  # expected level 0-2

Many items at once: basal-run --input items.jsonl --output answers.jsonl. Full API (multi, act, "evidence": true, "facts": "auto", option keys): API and features.

Engines

The status column refers to basal-1.5 (4.5B); basal-1.5-mini uses the same engines and API, and its own measurements are listed where available.

basal-serve is the decision layer in every row. vLLM, SGLang, MLX, Ollama and llama.cpp run the weights; the typed answers (a calibrated probability for each of your options, both option orders, the state shared by several questions, multi, act and facts) come from basal-serve --mode <engine> in front of them, with the same HTTP API in every mode. A plain ollama run or llama-cli gives free text only. Evidence spans need the PyTorch engine (fast, eager); vLLM and SGLang start with a 4,096-token context (longer states are refused); raise it with --max-len (e.g. 32768 for long documents). Install and first request for each engine: quick start.

Speed

One decision = one question in both option orders (the default), median latency. About 3 GB of GPU memory for the bf16 weights.

Release check (one H100, basal-serve --mode fast, bf16, HTTP load test): one question 10.9 ms p50 / 15.9 ms p95, 155.8 decisions/s with 32 concurrent clients (basal-1.0-1.5B was not re-measured on that machine).

Per GPU, measured with basal-1.0-1.5B (the same architecture: both are fine-tuned from speakleash/Bielik-1.5B-v3.0-Instruct; offline basal-bench with the basal-1.0 engine, batch size 1, throughput with 32 option-order passes per forward). It shows what each GPU does with this model size; it is not a measurement of basal-1.5-mini.

Apple Silicon (MLX, Apple M4 Pro, 24 GB): 196 ms per decision with basal-1.5-mini.

Several questions per request share the state: in an in-process engine comparison on one H100 (4.5B, same node and requests) 5 questions take 37.8 ms instead of 111.7 ms as separate prompts, 12 questions 80.3 ms instead of 222.0 ms (SOAM). Every GPU we measured: HARDWARE.md.

Files

• model.safetensors, tokenizer, chat_template.jinja: the model (Llama architecture, 32 layers, 32k Polish vocabulary)
• CALIBRATION.json: per-type temperatures and confidence thresholds for 1% / 5% error (applied by the basal server, also in the vLLM, SGLang, MLX and Ollama modes)
• evidence_head.pt: the pointer head behind "evidence": true (experimental)
• basal.json: prompt format and readout protocol

How it works

The model receives a fixed chat prompt with the state, the question and lettered options; the assistant turn is prefilled with {"answer": " and the decision is the softmax over the next-token logits of the option letters only (one forward pass, no text generation). The server asks every question with the options in original and reversed order and averages the two distributions (this reduces sensitivity to option order), then applies the calibrated temperature of the question type from CALIBRATION.json, fitted on exactly this averaged prediction.

Use the confidence. CALIBRATION.json stores confidence thresholds chosen on calibration data, before testing, for a target error of 1% or 5% among accepted decisions; on the test data they accept 46.5% / 70.4% of decisions at 0.90% / 5.3% observed error. Accept decisions above the threshold automatically and route the rest to a person. For mini, use the 1% tier when the error budget matters; at the 5% target the observed error on the held-out test was 5.3%. The thresholds are validated on descriptions-only prompts ("option_keys": "hide") and yes/no questions; for other formats, refit them on your own labelled requests.

How to ask. One condition per yes/no question (or a multi with one label per condition); choice with described sides for named binary outcomes; ask in the language of the state; put the facts and rules in the state; offer "cannot be determined" when the state may not contain the answer; compute exact numbers in code (or send "facts": "auto" for Polish dates and amounts).

Training

Fine-tuned from speakleash/Bielik-1.5B-v3.0-Instruct for typed decisions in Polish and English: rules and deadlines, real documents, long documents, retrieval decisions, contracts, routing, ordinal scores, judging answers, action selection and "cannot be determined" answers. The recipe includes a reinforcement-learning (RL) stage. basal-1.5-mini is distilled from basal-1.5-max.

Limitations

• Validate on your own documents before relying on it: hidden and held-out test sets do not cover every domain.
• Polish world knowledge of a small model is limited: provide the relevant facts in the state. Legal rules change; the model does not know rules introduced after its training.
• Exact arithmetic and thresholds are a weak spot of one-pass decisions: compute them in code or use "facts": "auto".
• Averaging the original and reversed option order reduces, but does not remove, sensitivity to option order for three or more options. Up to 10 options per question.
• act and evidence spans are experimental (features).
• Decisions with serious consequences for people should be reviewed by a person.

Citation

@software{kinas2026basal15,
  title   = {basal-1.5: typed-decision models and inference engine for Polish and English},
  author  = {Kinas, Remigiusz},
  year    = {2026},
  version = {1.5.0},
  url     = {https://github.com/rkinas/basal}
}

The method of the previous release is described in the basal-1.0 technical report (PDF, doi:10.5281/zenodo.23022986).

License and attribution

Apache-2.0. Fine-tuned from speakleash/Bielik-1.5B-v3.0-Instruct (Apache-2.0).