--- license: apache-2.0 base_model: Qwen/Qwen3.5-4B library_name: transformers language: - en tags: - decision-model - qwen3_5 - transformers --- # Jet-4B **v6.2.0 · released 2026-09-26 · focused continuation, selected step 250.** Jet-4B, part of the [Jet](https://quaedra.com/jet) family, returns typed decisions and probabilities from supplied options without generating free-form answers. This is the **full merged BF16 model**, not an adapter, fine-tuned from Qwen3.5-4B. No separate base-model download is required. It replaces v6.1 in `quaedra/jet-4b`; previous releases remain accessible by their version tags. ## Run Linux + NVIDIA CUDA, Python 3.12. Verified with PyTorch 2.11.0+cu128, Transformers 5.17.0 and flash-linear-attention 0.5.2. ```sh hf download quaedra/jet-4b --revision v6.2.0 --local-dir jet-4b cd jet-4b python -m pip install -r requirements.txt python jet.py <<'JSON' {"state":"I was charged twice this month.","questions":{"topic":{"type":"choice","instructions":"What is the primary issue?","criteria":{"billing":"billing or payment problem","bug":"the product is broken"}}}} JSON ``` ```python from jet import Jet model = Jet() result = model.decide("The item arrived broken.", { "damaged": {"type": "noul", "instructions": "Is the item damaged?"} }) ``` | Type | Input | Output | |---|---|---| | `choice` | 2–255 named options | Selected key and probabilities | | `score` | 2–10 ordered levels | Expected zero-based score, selected level, probabilities | | `noul` | Yes/no question | Probability of yes | Use the included native prompt and restricted label-token readout. A generic text-generation pipeline does not implement this API. Questions are processed separately. **The runtime now accepts complete prompts up to 16,384 tokens**; inputs and options are never silently truncated. The longest reconstructed API-Bank input (11,495 tokens) passed the standalone runtime check, but full API-Bank accuracy has not been measured for this release. Context feasibility does not establish long-context quality across tasks. FLA is enabled; convolution uses the PyTorch fallback. `confidence` is normalized inverse entropy, not a calibrated correctness probability. Temperatures are inherited from v6.1 and were not refitted or established as calibrated for v6.2. ## Training and selection The parent is full merged Jet-4B v6.1 at `f446b82727be57da348bb46eccf211414294ab3e`. Parent shard hashes and the exact adapter hash are in `merge-provenance.json`. A fresh rank/alpha-16 correction LoRA was trained on that parent; the model was not reset to the original Qwen weights. - 4,000 examples: 800 banking intent, 800 entity-specific financial sentiment, 400 sarcasm/literal, and 2,000 broad retention examples. - BF16 backbone / FP32 adapter, dropout .05, microbatch 1, accumulation 4, seed 260925; two trials of 1,000 updates at peak learning rates 3e-6 and 8e-6. - Validation-selected winner: **3e-6 at step 250**. The higher-rate trial failed the selection guards and is not included in this release. - Selection used 915 examples and an objective of 70% mean focus metrics plus 30% retention accuracy; every family must stay within two percentage points of its initial metric. The winner was frozen before final holdout evaluation. - Banking uses the training partition of BANKING77, financial supervision uses SEntFiN, and sarcasm uses author-labeled iSarcasm training examples. Financial headlines and tweet/rephrase groups stay in one split. ## Full-model holdout results The results below were re-measured on the **full merged model**, after BF16 rounding. Focus holdouts exclude the previous local input corpus listed in the data audit; retention is reused. Small sample sizes mean these modest differences do not establish statistical significance or broad benchmark improvement. | Holdout / metric | Cases | v6.1 | v6.2 merged | |---|---:|---:|---:| | Banking / accuracy | 154 | 74.68% | 74.68% | | Entity sentiment / macro-F1 | 180 | 71.16% | 71.21% | | Sarcasm / positive-class F1 | 80 | 46.81% | 50.00% | | Retention / accuracy | 500 | 93.40% | 93.40% | Financial sentiment is a SEntFiN transfer holdout, **not the FinEntity benchmark**. Banking and sarcasm rows above are local source holdouts, not their public test benchmark scores. Exact content/group exclusions do not establish semantic or pretraining decontamination. Benchmarks informed training focus. **Decision Index 0.3 (official, 2026-10-07): 40.01, rank 52 of 113.** Run by the index maintainers on the full suite (110,201 requests, 42 benchmarks; 243 requests exceed the 16,384-token prompt limit): public 42.17, same skills 40.93, new domains 34.46. Within the index's tie band of the other entries on the same 4B base. Strongest in Tools & Automation (62.9), weakest in Knowledge & Reasoning (25.4); 66.0% accurate at 80.1% mean confidence (ECE 0.141). [Details](https://quaedra.com/jet/docs/decision-index) · [Leaderboard](https://huggingface.co/spaces/multimodalart/jev-decision-index) The earlier 25-benchmark comparison belongs to the v6.1 candidate and must not be attributed to this release. ## Merge verification FP32 B@A corrections were added to the already merged parent and rounded to BF16. All 426 tensors loaded without missing, unexpected or mismatched keys; 248 modules received updates. Nine shards contain 8,411,510,272 parameter bytes. On 157 fixed verification cases, **0 selected answers changed** between the adapter and merged model. The largest probability difference was **4.76 percentage points**. These checks include choice, score, noul and a long API-Bank input. The full 914-case holdout passed the predeclared release guards. See evaluation.json, merge-validation.json and release-manifest.json. Merge equivalence is approximate. [Website](https://quaedra.com/jet) · [Docs](https://quaedra.com/jet/docs/) · [Source and experiment records](https://github.com/quaedra/jet) · [Training protocol](https://github.com/quaedra/jet/blob/main/experiments/jet-focused-20260925/protocol.md) · [Release validation](https://github.com/quaedra/jet/blob/main/experiments/jet-release-20260926/protocol.md) Apache-2.0 model/runtime; source datasets retain their own licenses. Dataset rows are not redistributed here. Quality outside the evaluated domains is not established.