Jev-Style-2B-Decision-v4-MLX

Local classification, routing and fixed-choice scoring with JevStyle.

2B v4 training included soft labels distilled from TypeSafe Jev 1.14 through its public API. JevStyle is independently maintained by Chaoliang Yan, with no TypeSafe affiliation or endorsement. The Python package does not bundle raw teacher-response datasets; this does not negate the use of teacher outputs in training.

Official local installation — no hosted API key or agent simulation: For text and images, install python -m pip install -U "jev-style[torch]" and run jev-style serve --release 2b --backend torch. MLX and GGUF builds are text-only. The package runs downloaded JevStyle weights on your machine. Step-by-step installation and verification. TypeSafe/OpenRouter Jev access and skills that simulate decisions with another agent model are separate execution paths.

Hosted service is separate from local inference. On 2 November 2026 its public health endpoint reported inference ready. This dated observation is not an end-to-end request test or permanent uptime claim. Check current service status and limits or live health when using the hosted API.

Choose a workflow: Python text classification, local routing tools and Apple silicon, receipt or screenshot yes/no decisions, reproducible routing comparison. These guides distinguish suitable alternatives and published evidence; they do not establish universal superiority.

Website: jevstyle.com · GitHub: jev-style

Jev-Style decision series: v1 · 2B → v2 · 2B → v3 · 0.8B → v3 · 2B → v4 · 2B

Jev-style decisions, native on Apple silicon. These are the MLX builds of Jev-Style-2B-Decision-v4 for text and JSON states: bf16 (3.76 GB) and 8-bit (2.00 GB) in one repository, with one runtime (jev_style_decision_mlx.py; choose the folder with --model-dir bf16 or --model-dir 8bit). Images need the main repository's PyTorch runtime. Full results, protocols, training data and licences are on the main model card.

_{Results of the model, measured with the main repository's PyTorch runtime: Decision Index public suite 0.3 with the kit's own runner and scorer (bfloat16 on one A100, one complete run of 140,178 requests; v3: its public numbers on the 0.4.1 board); JevBench v1.4.1 public items with the official harness (float32 on Apple MPS, self-run once, 95% CI 68.9–80.0%; v3: the number on its own card). Neither is an official board entry. These MLX builds were not re-run on the benchmarks; they reproduce that runtime's scores within the pre-declared gates in the table below. Protocols: main card.}

Precisions and parity

• Each folder holds model.safetensors, its config.json, the tokenizer and macjev_norms_fp32.safetensors (FP32 norm weights, 0.42 MB, required). The top level holds the shared runtime jev_style_decision_mlx.py, readout_config.json, release_config.json, the sha256 manifest.json, LICENSE, NOTICE, THIRD_PARTY_NOTICES.md and a copy of bf16/config.json (for the Hub's download counter). Converted with mlx 0.32.2 and mlx-lm 0.31.3.
• Which folder: bf16/ for closeness to the FP32 reference (max abs Δp 0.036 vs 0.048, median 0.0006 vs 0.0009); 8bit/ at about half the size when memory is tight: it made the same call as the FP32 reference on 99.9% of the 1,000 questions.

_{Reference: the main repository's reference scorer in float32 on CUDA (TF32 off, plain PyTorch Qwen3.5 layers), exact block attention, on the released weights, text only; gates declared before any format was built or scored. Gate fixture: 1,000 questions from our own held-out development rows (≤ 4,096 tokens; 645 choice, 316 true/false, 39 score; up to 151 options), accuracy = top-1 in the gold set. Long fixture: 70 requests / 97 questions, development rows of 4,097 to 32,768 tokens plus edge cases (255 options, 15 catalogue questions, an input of exactly 32,768 tokens); the column counts questions with the same top-1 as the reference. On the long fixture the scores of every question (bf16, 97 / 97) are closer to the block-attention reference than to plain-causal and prefix-causal controls of the same scorer, so the runtime runs the attention the model was trained with, and in both folders score_many gave exactly the scores of each question asked alone. MLX scored once on an Apple M1 Max. Sizes are the weight files (GB = 10^9 bytes). Records: validation/parity/.}

Speed

On an Apple M1 Max (MLX bf16), a single question about an input of up to 1,024 tokens (median 250) took a median 233 ms, and one about 16,385–32,768 tokens (median 21,646) 15.52 s. score_many computes the state once for all questions about it.

• Pick a precision folder by size and closeness to FP32 (table above). bf16 and 8-bit were timed one after the other on the same machine; 8-bit was the slower folder in every row.

_{Apple M1 Max, 64 GB, macOS 15.7.5, mlx 0.32.2 / mlx-lm 0.31.3. Wall time of one scoring call (the input was rendered and the model loaded beforehand), the state computed from scratch, once per fixture request: the 1,000 gate-fixture requests and the single-question requests of the long fixture. The Mac was in normal use during the run (1-minute load average about 9), so treat the numbers as indicative.}

Quick start

pip install -U huggingface_hub
hf download chaoliangUNSW/Jev-Style-2B-Decision-v4-MLX --local-dir Jev-Style-2B-Decision-v4-MLX
cd Jev-Style-2B-Decision-v4-MLX
pip install -r requirements.txt        # mlx 0.32.2, mlx-lm 0.31.3 (exactly), transformers 5.17.0, tokenizers, numpy

Only one precision is needed: add --exclude "8bit/*" to the download for bf16 only, or --exclude "bf16/*" for 8-bit only. Pass the precision folder to the runtime with --model-dir bf16 or --model-dir 8bit (in Python, JevStyleDecisionMLX("bf16") or JevStyleDecisionMLX("8bit")).

# choice question, bf16 weights
python jev_style_decision_mlx.py --model-dir bf16 --state "Finder is open on ~/Reports. The user asked: email report.pdf to Ana." \
  --question "What should the agent do next?" \
  --options '{"attach_report": "attach report.pdf to a new email to Ana", "rename_report": null, "close_finder": "close the Finder window"}'

# true/false question about a JSON state, 8-bit weights
python jev_style_decision_mlx.py --model-dir 8bit \
  --state-json '{"app": "Mail", "draft": {"to": "ana@example.com", "attachments": []}}' \
  --question "Is the report attached?" --qtype noul

# score (ordinal) question, uncalibrated probabilities (T = 1.0)
python jev_style_decision_mlx.py --model-dir 8bit --state "The deploy failed twice with the same migration error." \
  --question "How risky is retrying now?" --qtype score --options '["no risk", "some risk", "high risk"]' --temperature 1.0

# full typed question as JSON, first checking the sha256 of the runtime files and the chosen precision folder (manifest.json)
python jev_style_decision_mlx.py --model-dir bf16 --verify --state "cart: 3 items" \
  --question '{"t": "choice", "ins": "Next step?", "crit": {"checkout": null, "keep_shopping": null}}'

The output has answer, probabilities and raw scores per option, temperature (the calibrated temperature of the question type: choice 0.770, true/false 0.825, score 0.584), top_probability, entropy_concentration, input_tokens, state_tokens, head_tokens, blocks and catalogue_overflow.

from jev_style_decision_mlx import JevStyleDecisionMLX
m = JevStyleDecisionMLX("8bit")                # the 8-bit precision folder
state = {"screen": "Settings > Wi-Fi", "goal": "join the network Office-5G"}
qs = [{"t": "choice", "ins": "Next action?", "crit": {"tap_office_5g": None, "toggle_wifi_off": None, "go_back": None}},
      {"t": "noul", "ins": "Is Wi-Fi turned on?", "crit": None}]
for r in m.score_many(state, qs):          # the state is computed once and reused for every question
    print(r["answer"], round(r["top_probability"], 4))
m.decide(state, qs[0])                     # same state again: reused (m.last_timing["state_reused"] is True)
m.close()

• Batch mode: --jsonl requests.jsonl (or - for stdin), rows {"id"?, "state", "question", "options"?, "qtype"?, "temperature"?}; consecutive rows with an identical state share one state computation, and a malformed row gets an "error" field while the batch continues.
• Long inputs. The complete input can be up to 32,768 tokens, with no separate question/options cap and up to 255 options per question; question and options over 2,048 tokens use a numbered-option catalogue. In the parity run (table above) both folders scored an input of exactly 32,768 tokens and a 255-option question (8,449 tokens, catalogue layout). A larger input raises InputBudgetError (CLI exit status 2); nothing is truncated.
• mlx-lm must be exactly 0.31.3. The runtime corrects two Qwen3.5 numerics inside mlx-lm and checks the source it patches; other versions are refused with the install command. It also refuses to run without macjev_norms_fp32.safetensors or readout_config.json, and never falls back to stock norms or T = 1.
• The input format, block attention and the readout are described on the main card.

With the jev-style package

jev-style 0.6.0 or later downloads this repository at a pinned revision and runs this runtime behind a local systemone-compatible API, with a Playground, demos and MCP tools:

pip install "jev-style[mlx]"          # with mlx-lm 0.31.3
jev-style serve --release 2b-v4       # bf16 on Apple silicon; --precision 8bit for the 2.00 GB folder

from jev_style import JevStyle, noul

js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-2B-Decision-v4-MLX", precision="8bit")
out = js.decide({"app": "Mail", "draft": {"to": "ana@example.com", "attachments": []}},
                {"attached": noul("Is the report attached?")})

Scope and limits

• Runtime required. mlx_lm.generate can load the weights in bf16/ or 8bit/ (the repository root holds no weights) but cannot produce the decision scores, and it would run causal attention. Use jev_style_decision_mlx.py.
• Text only. These files hold the language model; images in the state need the PyTorch runtime of the main repository.
• 32,768 tokens is the limit for the whole input (state + question + options + readout); at most 255 options per question.
• Decision Index. The training data includes the official train splits of several benchmarks that the index tests on, so results on those benchmarks are not zero-shot; they are named on the main card.
• Decisions only. The model scores the options you give it. It does not generate text and takes no actions.

Disclaimers and licence

Apache-2.0. Built on Qwen/Qwen3.5-2B (Apache-2.0); NOTICE lists the modifications. Soft labels distilled from TypeSafe Jev 1.14 via its public API are the targets of part of the training rows; no Jev weights or code are included. Some training data has terms of its own and some training rows were written by Anthropic Claude and OpenAI GPT models, whose providers' terms of use may restrict how models trained on them may be used: see Training data and licences on the main card. The runtime contains a modified copy of one mlx-lm 0.31.3 function (the Qwen3.5 GatedDeltaNet.__call__; MIT License, Copyright Apple Inc.); its copyright and permission notice is in THIRD_PARTY_NOTICES.md. Not affiliated with, endorsed by or connected to TypeSafe AI or Jev, the Laya authors or the Qwen team.

AI disclosure: code written with AI coding assistants (Claude Code) under my direction; I designed the project, trained the models and verified the results.

Contact

I welcome internship, employment, and research collaboration opportunities. Please contact me at yanchaoliang369@gmail.com.

欢迎提供实习、工作及科研合作机会，请邮件联系：yanchaoliang369@gmail.com。