Remek

basal-omni-1.0-max-FP8

README

License: apache-2.0

Measured against bf16

Agreement is the share of decisions where this checkpoint picks the same option as the bf16 release model, served as released (--mode eager, bf16), on the development set dev v2 (10,606 decisions), both on one RTX PRO 6000 96 GB (Blackwell). Bars (the basal port gate for this tier): agreement on 2-10 options >= 0.9552, agreement where bf16's margin >= 0.2 >= 0.9893, accuracy within 1 point, ECE at most +0.01.

Table with columns: GPU, agreement (options 2-10), agreement at margin >= 0.2, accuracy (2-10 options), ECE (2-10 options), gate
GPU	agreement (options 2-10)	agreement at margin >= 0.2	accuracy (2-10 options)	ECE (2-10 options)	gate
RTX PRO 6000 96 GB (Blackwell)	0.9688	0.9893	0.8589 -> 0.8555 (-0.34 pt)	0.0192 -> 0.0182	PASS

All 10,606 decisions (up to 255 options): the same option in 96.0%, accuracy 0.8195 -> 0.8159 (-0.36 pt). Most changed decisions are near-ties of bf16 (margin below 0.05: 56% agreement; margin 0.5 or more: 99.7%).
Automation (calibration test, 8,560 decisions, each package with thresholds fitted on its own readouts on the same GPU): at 1% target error this checkpoint decides 45.4% of decisions automatically (bf16: 47.2%); at 5%, 82.5% (bf16: 83.0%).
Repeatability. In --mode vllm a decision can change between runs: vLLM's numbers depend on what the server computed before (prefix cache, batching). Two runs of the first 2,000 dev v2 decisions (one at a time, and 8 in flight) give the same top answer in 96.9% of them (99.4% where bf16's margin is 0.2 or more); the released engine v2.1.0 against v2.0.0 differs by the same amount. For repeatable answers set VLLM_BATCH_INVARIANT=1 (see Use): two runs of basal-omni-1.0-max-FP8 at 32 requests in flight then gave the same top answer on all 2,000 decisions, at about 23% fewer decisions per second.
The checkpoint is a decision-layer port of the same model, not a new model; its benchmark results (Werdykt v1 0.823, the Polish and English sets) are on basal-omni-1.0-max. This port is not scored separately on them.

Use

Install the basal engine with vLLM 0.31.0 (its PyPI build uses CUDA 13: NVIDIA driver 580 or newer):

bash

uv venv --python 3.12 --allow-existing ~/basal-vllm && source ~/basal-vllm/bin/activateuv pip install vllm==0.31.0 "basal @ https://github.com/rkinas/basal/archive/refs/tags/v2.5.4.tar.gz"basal-serve --model Remek/basal-omni-1.0-max-FP8 --mode vllm --max-len 16384 --gpu-memory 0.9 --port 8000

Then ask questions exactly as with basal-omni-1.0-max (choice, noul, score, multi, act, tool, and the task types; up to 255 options).

GPUs: FP8 needs sm89 or newer (Ada, Hopper, Blackwell). Checked on one RTX PRO 6000 96 GB.
Memory: the weights take 14 GB. With --gpu-memory 0.9 on the RTX PRO 6000, vLLM keeps 211,329 tokens of state cache (bf16 through vLLM: 182,836).
Text only. --mode vllm reads text states; images and audio ("media") need the bf16 model with --mode eager --media on.
One option order. The shipped calibration is fitted for one option order (the default).
Repeatable answers: start the server with VLLM_BATCH_INVARIANT=1 basal-serve ... (about 23% fewer decisions per second); without it a decision near a tie can change between runs and with the load (--deterministic is not available in --mode vllm).

Speed

One RTX PRO 6000 96 GB, one option order, 16 requests in flight (basal-loadtest); latency p50 / p95 is one request at a time:

Table with columns: served as, one question over a ~1,500-token state, 5 questions over one 3,000-10,000-token state
served as	one question over a ~1,500-token state	5 questions over one 3,000-10,000-token state
this checkpoint, `--mode vllm`	8.7 decisions/s, 118 / 177 ms	4.8 questions/s, 1.07 / 2.05 s per request
bf16, `--mode vllm`	5.6 decisions/s, 176 / 263 ms	3.4 questions/s, 1.51 / 2.92 s per request

How it was made

Recipe: NVIDIA ModelOpt 0.47, FP8 (E4M3) weights and activations with static per-tensor scales, calibrated on 2,048 Polish and English decision prompts in the model's prompt format; decoder layers 0, 1, 46 and 47, the embeddings, the output head and the image and audio encoders stay bf16 (301 of the language model's linear layers are FP8).
Calibration: CALIBRATION.vllm.json (and its copy CALIBRATION.json) is fitted on this checkpoint's own vLLM readouts with the bf16 model's method and splits: per-type temperatures and certified thresholds for 1% and 5% error, one option order. basal-serve --mode vllm loads CALIBRATION.vllm.json. You can refit it on your own labelled data with basal-calibrate.

Files

model-*.safetensors, config.json, hf_quant_config.json: the ModelOpt FP8 checkpoint (vLLM reads it natively).
Tokenizer, chat_template.jinja, processor_config.json: as in the bf16 model.
CALIBRATION.vllm.json, CALIBRATION.json: the calibration fitted on this checkpoint.
basal.json: prompt format 2 and the readout protocol.

Limitations

Experimental, as the bf16 model: validate it on your own documents before relying on it.
Text only in this checkpoint (see Use).
FP8 changes some decisions where the model is unsure (see the table); decisions it is sure about are almost unchanged.
Exact arithmetic and thresholds are a weak spot of one-pass decisions: compute them in code.
Decisions with serious consequences for people should be reviewed by a person.

Citation

bibtex

@software{kinas2026basal20,  title   = {basal-2.0: typed-decision models and inference engine for Polish and English},  author  = {Kinas, Remigiusz},  year    = {2026},  version = {2.0.0},  url     = {https://github.com/rkinas/basal}}

License and attribution

Apache-2.0. Quantised from basal-omni-1.0-max, fine-tuned from google/gemma-4-12B-it (Apache-2.0).

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Model Details

Model Provider

Remek

Model Tree

Base

Remek/basal-omni-1.0-max

Quantized

this model

Input Modalities

TextAudioImageVideo

Output Modalities

Text

Supported Functionality