Qev-0.8B — PyTorch decision adapter

This repository publishes the Qev v0.3.0 Snake continuation checkpoint, trained from the earlier Qev decision adapter with Snake supervision and replay of the original general decision tasks. Its local training name is qev-snake-0.8b; qev-0.8b remains the API model alias. This is the newer checkpoint, not the original v0.2 weights.

Qev adds a language LoRA adapter and a candidate pointer head to the complete Qwen3.5-0.8B multimodal foundation. It scores supplied answer options and returns typed decisions through a Jev / TypeSafe-style API. Native text, image and sampled-video generation disables the decision adapter and uses the frozen foundation.

This is an adapter checkpoint, not a standalone Transformers pipeline model. Use the Qev runtime. The foundation is fetched separately at the pinned revision on first use. A normal transformers.pipeline, standalone PEFT loader, or ollama run does not load the complete Qev decision system.

The corresponding full Apple Silicon export is twainsk/qev-0.8b-mlx.

Quickstart

Install uv and Git, then run:

git clone https://github.com/loadchange/qev.git
cd qev
uv sync --python 3.12
uv run hf download twainsk/qev-0.8b --local-dir models/qev-snake-0.8b

# Run terminal Snake. PyTorch automatically selects CUDA, MPS or CPU.
uv run qev snake --model models/qev-snake-0.8b

# Typed decision example and local HTTP server
uv run qev predict --model models/qev-snake-0.8b --request examples/request.json
uv run qev serve --model models/qev-snake-0.8b --port 8008

The Qev loader accepts local paths: download the repository before passing its local directory to --model. Run these commands from the cloned project directory, or provide absolute checkpoint and request paths. NVIDIA CUDA is the evaluated PyTorch path; Apple Silicon users should prefer the MLX release.

中文： 下载后运行 uv run qev snake --model models/qev-snake-0.8b 即可在终端观看模型逐步决策。空格暂停/继续，N 单步，+ / - 调速，Q 或 Ctrl-C 退出。首次运行需要下载固定版本的 Qwen 基座。

The server binds to 127.0.0.1 by default. Typed decisions use POST /v1/systemone; adapter-disabled native generation uses POST /v1/chat/completions. See the API examples. Checkpoint aliases do not switch the inference backend.

Contents and requirements

• qev_config.json: Qev format version 2, architecture, pinned foundation reference and calibrated temperature.
• adapter/adapter_model.safetensors and adapter/adapter_config.json: unmerged rank-16 LoRA weights and configuration.
• pointer.safetensors: the learned 256-dimensional candidate pointer head.
• tokenizer/ and processor/: text and original image/video preprocessing assets.
• reports/: data provenance and evaluation summaries; release_manifest.json lists release file sizes and SHA-256 hashes.
• Root LICENSE and NOTICE: license and attribution documents. Training rows and generated PEFT template cards are not included.

The published files total about 85.6 MB. They do not contain the frozen foundation weights. Qev loads Qwen/Qwen3.5-0.8B at revision 2fc06364715b967f1860aea9cf38778875588b17, so allow additional download, storage and runtime memory for that model. For offline use, first populate the Hugging Face cache with that exact revision.

Use Python 3.12 or later and the Qev project's dependency lock. The validated software family is Transformers 5.17, PEFT 0.21 and PyTorch 2.10 or later; training used PyTorch 2.11.0 with CUDA 12.8. All decision adapters remain separate from the original foundation. Merging them would change the native generation path and is unsupported by Qev.

Training

The original foundation's 852,985,920 parameters remained frozen. Qev continued training the existing 11,346,944 LoRA and pointer parameters using supervised cross-entropy; this was not reinforcement learning, and no private Jev labels were used.

Item	Value
Snake train / calibration / development questions	12,000 / 500 / 500
Replayed original train / calibration / development questions	5,892 / 620 / 880
Combined train / calibration / development questions	17,892 / 1,120 / 1,380
Snake trajectory sizes	6×6, 8×8, 12×12, 16×16
Training	2 epochs, batch 8, accumulation 2, learning rate 3e-5
Compute	NVIDIA A100 40 GB, 2,238 updates, about 1,485 optimization seconds
Precision	FP32 master weights, CUDA BF16 autocast, FP32 pointer
Fitted calibration temperature	2.82842712474619

Snake labels come from a deterministic teacher that uses the same explicit fields visible to the model. The environment supplies collision and food facts, static BFS reachable space, tail connectivity, food path distance and recent visit counts. No teacher direction, preferred-action marker or ranking is inserted in runtime input. The three non-reversing candidates include potential collisions, and runtime execution uses the model's argmax directly without a safety override. Snake is a text-feature task; the displayed board is not supplied as an image.

The dataset manifest SHA-256 is f41151c68d6465b90bb8ea66ca0ea8611a6b37ed48596ba9484abc8ef4deda33. Full provenance, source licenses, split isolation and reproduction commands are recorded in the training documentation and Snake model report.

Evaluation

The following comparison uses the same A100 and CUDA BF16 arithmetic before and after continuation:

Development measure	Questions	Parent	This checkpoint
Snake teacher-action agreement on held-out game seeds	500	73.60%	98.40%
Original general decision accuracy	880	81.25%	81.70%

Closed-loop results use held-out seeds, the same spatial features and a 500-step cap per game. Torch advances eight independent games per batch. The model chooses every executed action.

Board / held-out seeds	Parent mean food	This checkpoint mean food	Parent collisions	This checkpoint collisions
8×8 / 10000–10019, 20 games	3.10	42.90	20 / 20	0 / 20
12×12 / 10000–10007, 8 games	0.625	42.00	8 / 8	0 / 8

All 28 new-model games reached the step cap; none filled the board. These are finite closed-loop tests, not a guarantee of collision-free or optimal play. The corresponding MLX export averaged 42.6 food over five 8×8 games on an Apple M4. Numerical precision, batch size and action-dependent trajectories can change results. See the complete benchmark conditions and evidence.

Multimodal preservation and limitations

Full frozen-foundation hashes matched before and after training. On the same A100, adapter-disabled native generation produced identical token IDs for three fixed probes covering text, image and video. This is weight-preservation and limited regression evidence, not a comprehensive multimodal quality evaluation. Decision accuracy on image/video inputs has not been measured; multimodal decision probabilities remain uncalibrated.

The checkpoint is a small supervised decision experiment. General task coverage and Chinese task evidence are limited. It does not establish performance parity with Jev, TypeSafe or Laya; Jev compatibility refers to interface and answer types. It does not learn Snake geometry directly from pixels. The initial generic model, dataset provenance and additional limitations are documented in the original model card; v0.3.0 results above supersede the original checkpoint's metrics for this release.

License and attribution

Qev code, adapters and pointer weights are released under Apache-2.0. The Qwen3.5 foundation is separately distributed by its authors under Apache-2.0. Preserve the included LICENSE and NOTICE, and the upstream attribution documents where present.

Replayed public training records originate from jaredpalmer/kev-suites and its source datasets, whose declared licenses differ and include share-alike, other and undeclared terms. The Apache software/model declaration does not relicense those datasets or settle their downstream terms. This model repository is not a redistribution of the training dataset. See NOTICE and the data provenance manifest. Qev is an independent project and is not an official Jev or Qwen release.