| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3.6-35B-A3B |
| library_name: transformers |
| pipeline_tag: text-generation |
| inference: false |
| tags: |
| - sglang |
| - mixture-of-experts |
| - typed-classification |
| --- |
| |
| # Xor 1.2 |
|
|
| Xor 1.2 (`xor-1.2`) is a post-trained version of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) for typed decision tasks. It is served through a TypeSafe-compatible `/v1/systemone` API. |
|
|
| ## Changes from Xor 1.1 |
|
|
| - New post-trained weights (LoRA rank 16, merged into BF16 base weights) |
| - Serving bundle, API, calibration, and media handling are unchanged from Xor 1.1 |
| - Validated on one GPU (TP1) with the same pinned, patched SGLang runtime |
|
|
| ## Revisions |
|
|
| | Revision | Release | |
| |---|---| |
| | `main` | Latest stable release (currently Xor 1.1) | |
| | `release/xor-1.2` | Xor 1.2 | |
| | `v1.1` | Xor 1.1, immutable | |
| | `xor-v1` | Xor 1.0, immutable | |
|
|
| Pin a revision for reproducible results. `release/xor-1.2` is a branch; pin its commit hash for byte-exact reproducibility. |
|
|
| ```bash |
| hf download juspay/xor --revision release/xor-1.2 --local-dir xor-1.2 |
| ``` |
|
|
| ## Base model |
|
|
| | Field | Value | |
| |---|---| |
| | Base checkpoint | `Qwen/Qwen3.6-35B-A3B` | |
| | Base revision | `995ad96eacd98c81ed38be0c5b274b04031597b0` | |
| | Architecture | Mixture-of-experts causal language model | |
| | Total parameters | 35 billion | |
| | Activated parameters | Approximately 3 billion per token | |
| | Released precision | BF16 | |
| | Base license | Apache License 2.0 | |
| | Packaging | Fully merged weights; no adapter loading or merging is required | |
|
|
| ## Interface |
|
|
| The server accepts a state and a map of typed questions: |
|
|
| - `noul`: binary probability |
| - `choice`: categorical decision and full probability distribution |
| - `score`: expected ordinal score and full probability distribution |
|
|
| Requests may include an `images` array with up to eight image data URLs, or one `video` data URL. Typed questions can classify information from the supplied media and text. Send one kind of media per request: the API accepts images and a video together, but the model does not reliably tell them apart. |
|
|
| The complete request body must not exceed 8 MB, which leaves roughly 6 MB of media after base64 encoding. Larger requests are rejected with HTTP 422. |
|
|
| The serving layer performs deterministic single-token candidate readout, forward and reverse option-order evaluation, probability calibration, and schema conversion. The serving layer is part of the released inference configuration and must be used for reproducible results. |
|
|
| ## Quick start |
|
|
| Download the release, verify and extract the serving bundle, and start Xor on one GPU: |
|
|
| ```bash |
| hf download juspay/xor --revision release/xor-1.2 --local-dir xor-1.2 |
| |
| (cd xor-1.2/serving && sha256sum -c xor-1.2-serving.tar.gz.sha256) |
| |
| mkdir -p xor-1.2-runtime |
| tar -xzf xor-1.2/serving/xor-1.2-serving.tar.gz -C xor-1.2-runtime --strip-components=1 |
| |
| cd xor-1.2-runtime |
| cp .env.example .env |
| sed -i "s|^MODEL_DIR=.*|MODEL_DIR=$(cd ../xor-1.2 && pwd)|" .env |
| ./run.sh |
| ``` |
|
|
| `run.sh` verifies every model file against `checksums.sha256` before starting. When the smoke test succeeds, the API is available at `http://127.0.0.1:30002/v1/systemone`. |
|
|
| ```bash |
| curl -sS -X POST http://127.0.0.1:30002/v1/systemone \ |
| -H 'Content-Type: application/json' \ |
| --data @examples/request.json |
| |
| curl -sS -X POST http://127.0.0.1:30002/v1/systemone \ |
| -H 'Content-Type: application/json' \ |
| --data @examples/image-request.json |
| ``` |
|
|
| The setup requires Linux x86-64, the Hugging Face CLI, Docker Engine with a recent Docker Compose v2 (the bundle uses the service-level `gpus` key, which older Compose releases reject), the NVIDIA Container Toolkit, and approximately 120 GB of free disk space. |
|
|
| ## Public JEVBench self-run |
|
|
| Xor 1.2 was evaluated locally on the public tiers of [JEVBench](https://github.com/fstandhartinger/jevbench) using harness commit `1bcc55eb6c8cffde2306b3db03ede39b61c6152a`, the existing `typesafe` adapter, and one request at a time. |
|
|
| The run used the released serving bundle unmodified on 1 x NVIDIA H200 NVL (143 GB), tensor parallelism 1, data parallelism 1, with request caching disabled. |
|
|
| | Tier | Attempted | Valid | Correct | Accuracy | p50 | p95 | |
| |---|---:|---:|---:|---:|---:|---:| |
| | Easy | 48 | 48 | 48 | 1.0000 | 0.0679 s | 0.0709 s | |
| | Original | 72 | 72 | 69 | 0.9583 | 0.0670 s | 0.0701 s | |
| | Hard public | 111 | 111 | 91 | 0.8198 | 0.0832 s | 0.1845 s | |
| | **All public** | **231** | **231** | **208** | **0.9004** | 0.0691 s | 0.1616 s | |
|
|
| Across all 231 public decisions: macro accuracy 0.9070, Brier mean 0.1790, ECE 0.0733. Operational success, coverage, schema validity, and strict schema validity were 1.0000. |
|
|
| These are self-run public-tier results, not an official JEVBench rank. Latency is hardware-specific and was measured locally without network overhead. |
|
|
| ## Validated runtime |
|
|
| | Setting | Value | |
| |---|---| |
| | SGLang image | `prakhar1611/xor-sglang@sha256:94c48d2a6cc98dc456cf93f723707ea7dd81dddfe1061e823b348d68bbe8158f` | |
| | Upstream SGLang base | `lmsysorg/sglang@sha256:6bcaa47db52f78ce0d67863b8b2431221b79bc23204a80cad757fa819d00e921` | |
| | Tensor parallelism | 1 | |
| | Data parallelism | 1 | |
| | Validated GPU | 1 x NVIDIA H200 NVL, 143 GB | |
| | Maximum prefill tokens | 250,000 | |
| | Static memory fraction | 0.85 | |
|
|
| The runtime image adds a small fix to SGLang's candidate-logprob result handling that prevents a scheduler crash when scoring and plain generation requests share a batch. `Dockerfile.sglang-logprob-fix` in the serving bundle reproduces it from the upstream image. |
|
|
| ## Operational notes |
|
|
| The model files occupy approximately 66 GB. Each data-parallel worker loads a complete model replica. Alternative hardware and parallelism settings must be validated independently before publishing performance results. |
|
|
| Artifact hashes, the base revision, adapter and merge metadata, the runtime image, and known provenance gaps are recorded in `RELEASE_PROVENANCE.json` and `checksums.sha256`. |
|
|
| The service does not require an API key when bound to localhost. Remote deployments must add authentication, TLS, rate limits, and request-size limits at the ingress layer. |
|
|
