Jev-Style-0.8B-Decision-v3-GGUF

Jev-Style-0.8B-Decision-v3-GGUF

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

This is the text-only 0.8B v3 release, maintained by Chaoliang Yan. Its training sources are documented below. The separate 2B v4 release was trained in part on soft labels distilled from TypeSafe Jev 1.14 through its public API. JevStyle is independently maintained, with no TypeSafe affiliation or endorsement. Training provenance is release-specific.

Official local installation — no hosted API key or agent simulation: For local text, install python -m pip install -U "jev-style[mlx]" on Apple silicon, or python -m pip install -U "jev-style[torch]" for CPU/CUDA; then run jev-style serve --release 0.8b. The 0.8B release is 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.

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Choose your starting point

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Jev-Style decision series: v1 · 2B → v2 · 2B → v3 · 0.8B · GitHub: jev-style · Website: jevstyle.com · Collection: all v3 builds and demos

Run it locally, inside your agents: github.com/lawrence3699/jev-style serves this model behind a systemone-compatible API with a Playground, and adds six agent skills (npx skills add lawrence3699/jev-style), a Claude Code guard hook and MCP tools. pip install "jev-style", then jev-style serve --backend gguf --scorer /path/to/jev-score, or in Python:

from jev_style import JevStyle, noul
js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3-GGUF", quant="Q4_K_M", scorer="/path/to/jev-score")
js.decide("I was charged twice.", {"billing": noul("This is about billing.")})

Build the jev-score scorer once with build_jev_score.sh from this repo (see Quick start).

Jev-style decisions on your laptop. These are the GGUF builds of Jev-Style-0.8B-Decision-v3 for llama.cpp: 0.53 GB in 4-bit (Q4_K_M), with the same decision as full precision on 240 of 240 parity rows. Full results, protocols, training data and licences are on the main model card.

_{Laya: the best of its three official checkpoints, re-run by us on identical rows with their shipped temperatures; paired 95% CIs exclude zero for Banking77 and MASSIVE. ¹ English Laya, as published by the elcronos study. ² Laya's score as published on the JevBench board; it lies inside v3's 95% CI, so this lead is a point estimate. ³ Default input budget in the Laya README: 1,024 tokens for the multilingual and typed checkpoints, 512 for English. Jev (API) has higher accuracy than v3 on each of these sets where its accuracy is published. Protocols and confidence intervals: see the main model card.}

Reads long documents in one call. Up to 25,600 tokens of input, 25× Laya's 1,024-token default and 25× our 2B v2's prompt. On 1,280 real 24K-token items v3 answers 98.3% correctly, and accuracy stays flat from 1K to 24K tokens (preregistered claim, passed).

Also: ahead of Laya multilingual in 51 of 51 languages · +2.6 points over Laya's typed checkpoint on typed decisions, trained on the same split (how to read that number).

Try it in your browser →

Files

_{Top-1 agreement with the PyTorch FP32 reference on a 240-row mixed parity fixture (training-pool rows, 22 categories, English and Chinese) plus 6 extra long prompts. These rows test agreement between formats, not accuracy. Sizes are the exported files (GB = 10^9 bytes). F16 is the runtime's default and the backend used for the latency figures.}

Quick start

The files are standard Qwen3.5 text models, so any recent llama.cpp loads them. Chat or text generation does not give you the model's decisions, though. Decisions are read at one verdict slot per option, and the bundled scorer does exactly that.

pip install -U huggingface_hub
hf download chaoliangUNSW/Jev-Style-0.8B-Decision-v3-GGUF --local-dir jev-v3-gguf
cd jev-v3-gguf
pip install -r requirements.txt                 # tokenizers, numpy

# Build the scorer against llama.cpp (tested at commit 441df11f65ea0b6d0c72965aaf70c8241070ddcb or later).
git clone https://github.com/ggml-org/llama.cpp
git -C llama.cpp checkout 441df11f65ea0b6d0c72965aaf70c8241070ddcb
sh build_jev_score.sh llama.cpp                 # -> ./build/jev-score (Metal on macOS)
# Linux + CUDA: LLAMA_CMAKE_FLAGS="-DGGML_CUDA=ON" sh build_jev_score.sh llama.cpp

python jev_style_decision_gguf.py --quant Q4_K_M \
  --state "The film was excellent." \
  --question "What is the sentiment of this review?" \
  --options '["negative", "positive"]' --category general_sentiment

From Python, the API is the same as the main repository's runtime:

from jev_style_decision_gguf import JevStyleDecisionGGUF

m = JevStyleDecisionGGUF(".", quant="F16")      # or "Q8_0", "Q4_K_M"
r = m.decide(
    {"ticket": "I was charged twice for my subscription this month.", "customer_tier": "pro"},
    "Which team should handle this ticket?",
    options={"billing": "payments, invoices, refunds", "technical": "bugs and outages", "sales": "new purchases"},
    category="theme_routing",
)
print(r["answer"], r["probabilities"])
m.close()

• jev-score (jev_score.cpp) is a small libllama program that runs as a JSON-lines process. It requests logits only at the slot positions. With a shared prefix it decodes the state once and scores several questions on copies of it.
• decide_many sends all questions about one state in one request. The default, many_mode="exact", returns exactly what one decide call per question returns; it shares the state in whole 1,024-token blocks, so it saves time from 1,024-token states on. many_mode="batched" reads the whole state once and scores all questions together (the setting of the latency chart); its probabilities differed from decide by at most 0.002 in our tests, and a near-tied top answer can change.
• The runtime opens a 32,768-token context, which covers the 25,600-token input limit plus the question part. The whole input may be up to 25,600 tokens, and the question, options and readout up to 2,048. Over-budget inputs raise an error, and nothing is truncated.
• Long option lists (added 2026-09-26). When a choice question's options do not fit the 2,048-token budget together, the runtime scores them in option chunks. Each chunk is an ordinary question with the same text and a contiguous slice of the options, and the chunks are as few and as even as possible. The per-option scores of all chunks then go through one softmax (option_chunks in the result). Questions that fit are unchanged: on a 476-request test set, every one of them came back bit-identical to the previous runtime. Use --no-split-options (or split_options=False) to get the old error instead.
• The input format, the readout and the calibration temperatures are described on the main card.

<details> <summary><strong>Results and speed</strong></summary>

• 4-bit, 0.53 GB, same calls. The Q4_K_M file matches PyTorch FP32 on 240 of 240 parity rows, plus 6 of 6 prompts at about 16K and 25.6K tokens, and it is about 2.4× smaller than the 2B v2's Q4_K_M (1.27 GB).
• Up to 4.6× faster than a Laya-architecture engine when 10 questions share one 4K-token state (1,381 ms vs 6,364 ms with many_mode="batched"; the engine is our round-1 MacLaya-4K, one call per question, not an official Laya checkpoint), because in that mode the bundled scorer reads the state once. It also answers questions about 8K-token states in 2.3 to 2.6 s.
• 79.2% on 2,000 typed decisions, +2.6 points over Laya's typed checkpoint trained on the same split (paired 95% CI +1.0 to +4.2) and +5.7 over the 2B v2. In-domain, so it measures agreement with the dataset's teacher labels; see reading the typed number.

_{Latency: untrained identical-architecture Qwen3.5-0.8B export on llama.cpp GGUF F16, one call per state with all questions scored together (many_mode="batched"); comparison engine = round-1 MacLaya-4K, our own fine-tune of the Laya multilingual architecture (FP32 on Apple MPS, 4,096-token budget, one call per question), not an official Laya checkpoint; Apple M1 Max 64 GB, warm p50, idle run 2026-09-23. v3 parity rows are drawn from the training pool; 2B v1/v2 quantization numbers and sizes are as reported on their public GGUF cards (their own 500 held-out decisions), so no agreement gap is claimed. More protocol detail is on the main card.}

</details>

Licence

Apache-2.0. Built on Qwen/Qwen3.5-0.8B (Apache-2.0). Some training data has restrictive or unclear terms, and some training rows are outputs of OpenAI and Anthropic models. See "Training data and licences" on the main card. Not affiliated with TypeSafe AI, Jev, the Laya authors or the Qwen team.

Contact

I welcome internship, employment, and research collaboration opportunities. Please contact me at [email protected].

欢迎提供实习、工作及科研合作机会，请邮件联系：[email protected]。