Instructions to use apus-ailab/APUS-OpenJev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apus-ailab/APUS-OpenJev-v1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("apus-ailab/APUS-OpenJev-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update canonical model series and public download statistics
Browse files- README.md +29 -27
- README.zh-CN.md +29 -27
- bundle-manifest.json +6 -6
- series-downloads.json +2 -2
README.md
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@@ -141,6 +141,35 @@ Compute depth and output width address two different costs. The native low-effor
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The optimization reduces output-head work, not the context-processing backbone. Current vLLM latency results do not include candidate-only output-head execution. See the [Technical Report](TECHNICAL_REPORT.md#6-performance-where-the-savings-can-come-from) for the mechanism and execution boundaries.
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## 5. Minimal Inference
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The root [meta.yaml](meta.yaml) catalogs model variants and their configuration hashes. Include it when downloading a variant; the repository and existing model paths are unchanged. You can also use [download_model.py](download_model.py) with `--variant 9B-3000 --local-dir ./APUS-OpenJev-v1`; it selects and checks the variant at one fixed Hub revision. Hub download statistics count requests to selected metadata files, not unique users or completed weight transfers. Older subfolder-only clients can bypass this metadata.
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**Authors:** gumpcheng ([https://huggingface.co/xDAN2099](https://huggingface.co/xDAN2099)), zhangxu, [APUS AI-LAB](https://github.com/APUS-AI-Lab).
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<!-- OPENJEV-SERIES:START -->
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## Model series and public downloads
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[APUS-OpenJev-v1 Collection](https://huggingface.co/collections/apus-ailab/apus-openjev-v1-6ab1ee888eb002fcdd3a2825)
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Standalone repositories use standard root-level model files. This repository and its legacy download paths remain available.
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| Model | Checkpoint | Release |
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|---|---:|---|
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| [4B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B) | 5949 | BF16 standalone |
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| [9B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B) | 3000 | BF16 standalone |
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| 35B-A3B | 5949 | Merge/reload validation in progress |
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### Download statistics · 项目汇总
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Status: **current**. Observed: 2026-09-22T03:08:12.998307Z (UTC). Last checked: 2026-09-22T03:08:12.998307Z (UTC).
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| Model | Last 30 days | All time | Status |
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| --- | ---: | ---: | --- |
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| [4B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B) | 0 | 0 | current |
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| [9B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B) | 0 | 0 | current |
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| 35B-A3B | — | — | unpublished; excluded |
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| **Series total (project-computed)** | 0 | 0 | complete |
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-
Totals sum only the published canonical repositories in this snapshot, separately for each time window. Historical snapshots and family/mirror repositories are never added. This is a project-computed summary, not a native Hugging Face Collection counter. Hub download counters are not unique users.
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<!-- OPENJEV-SERIES:END -->
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The optimization reduces output-head work, not the context-processing backbone. Current vLLM latency results do not include candidate-only output-head execution. See the [Technical Report](TECHNICAL_REPORT.md#6-performance-where-the-savings-can-come-from) for the mechanism and execution boundaries.
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<!-- OPENJEV-SERIES:START -->
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## Model series and public downloads
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[APUS-OpenJev-v1 Collection](https://huggingface.co/collections/apus-ailab/apus-openjev-v1-6ab1ee888eb002fcdd3a2825)
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Standalone repositories use standard root-level model files. This repository and its legacy download paths remain available.
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- **4B · checkpoint-5949 · BF16**: [https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B)
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- **9B · checkpoint-3000 · BF16**: [https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B)
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- **35B-A3B · checkpoint-5949**: Merge/reload validation in progress; link available after publication.
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### Download statistics · 项目汇总
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Status: **current**. Observed: 2026-09-22T03:10:37.132203Z (UTC). Last checked: 2026-09-22T03:10:37.132203Z (UTC).
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| Model | Last 30 days | All time | Status |
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| --- | ---: | ---: | --- |
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| [4B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B) | 0 | 0 | current |
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| [9B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B) | 0 | 0 | current |
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| 35B-A3B | — | — | unpublished; excluded |
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| **Series total (project-computed)** | 0 | 0 | complete |
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Totals sum only the published canonical repositories in this snapshot, separately for each time window. Historical snapshots and family/mirror repositories are never added. This is a project-computed summary, not a native Hugging Face Collection counter. Hub download counters are not unique users.
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<!-- OPENJEV-SERIES:END -->
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## 5. Minimal Inference
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The root [meta.yaml](meta.yaml) catalogs model variants and their configuration hashes. Include it when downloading a variant; the repository and existing model paths are unchanged. You can also use [download_model.py](download_model.py) with `--variant 9B-3000 --local-dir ./APUS-OpenJev-v1`; it selects and checks the variant at one fixed Hub revision. Hub download statistics count requests to selected metadata files, not unique users or completed weight transfers. Older subfolder-only clients can bypass this metadata.
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**Authors:** gumpcheng ([https://huggingface.co/xDAN2099](https://huggingface.co/xDAN2099)), zhangxu, [APUS AI-LAB](https://github.com/APUS-AI-Lab).
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README.zh-CN.md
CHANGED
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@@ -141,6 +141,35 @@ tags:
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这一优化减少的是输出头工作,不省略上下文骨干计算;当前 vLLM 延迟结果尚不包含候选专用输出头的收益。机制和执行范围见[技术报告](TECHNICAL_REPORT.md#6-performance-where-the-savings-can-come-from)。
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## 5. 最小推理示例
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根目录 [meta.yaml](meta.yaml) 提供模型版本及配置哈希;下载指定版本时请同时保留它,仓库与原模型路径不变。也可运行 [download_model.py](download_model.py),参数为 `--variant 9B-3000 --local-dir ./APUS-OpenJev-v1`,在同一固定 Hub revision 选择并校验模型。Hub 下载统计按指定元数据文件的请求计数,不等同独立用户或完整权重下载;旧版仅下载子目录的客户端可能绕过此元数据。
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**Authors:** gumpcheng ([https://huggingface.co/xDAN2099](https://huggingface.co/xDAN2099)), zhangxu, [APUS AI-LAB](https://github.com/APUS-AI-Lab)。
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<!-- OPENJEV-SERIES:START -->
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## 模型系列与公开下载统计
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[APUS-OpenJev-v1 Collection](https://huggingface.co/collections/apus-ailab/apus-openjev-v1-6ab1ee888eb002fcdd3a2825)
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独立模型仓库已采用标准根目录布局;原仓库与旧下载路径继续保留。
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| Model | Checkpoint | Release |
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|---|---:|---|
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| [4B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B) | 5949 | BF16 standalone |
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| [9B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B) | 3000 | BF16 standalone |
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| 35B-A3B | 5949 | Merge/reload validation in progress |
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-
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### Download statistics · 项目汇总
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-
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Status: **current**. Observed: 2026-09-22T03:08:12.998307Z (UTC). Last checked: 2026-09-22T03:08:12.998307Z (UTC).
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| Model | Last 30 days | All time | Status |
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| --- | ---: | ---: | --- |
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| [4B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B) | 0 | 0 | current |
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| [9B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B) | 0 | 0 | current |
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| 35B-A3B | — | — | unpublished; excluded |
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| **Series total (project-computed)** | 0 | 0 | complete |
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-
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-
Totals sum only the published canonical repositories in this snapshot, separately for each time window. Historical snapshots and family/mirror repositories are never added. This is a project-computed summary, not a native Hugging Face Collection counter. Hub download counters are not unique users.
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<!-- OPENJEV-SERIES:END -->
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这一优化减少的是输出头工作,不省略上下文骨干计算;当前 vLLM 延迟结果尚不包含候选专用输出头的收益。机制和执行范围见[技术报告](TECHNICAL_REPORT.md#6-performance-where-the-savings-can-come-from)。
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<!-- OPENJEV-SERIES:START -->
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## 模型系列与公开下载统计
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+
[APUS-OpenJev-v1 Collection](https://huggingface.co/collections/apus-ailab/apus-openjev-v1-6ab1ee888eb002fcdd3a2825)
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+
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+
独立模型仓库已采用标准根目录布局;原仓库与旧下载路径继续保留。
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- **4B · checkpoint-5949 · BF16**: [https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B)
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- **9B · checkpoint-3000 · BF16**: [https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B)
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- **35B-A3B · checkpoint-5949**: 合并与独立加载验证中,发布后提供链接.
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### Download statistics · 项目汇总
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Status: **current**. Observed: 2026-09-22T03:10:37.132203Z (UTC). Last checked: 2026-09-22T03:10:37.132203Z (UTC).
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| Model | Last 30 days | All time | Status |
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| --- | ---: | ---: | --- |
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| [4B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-4B) | 0 | 0 | current |
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| [9B](https://huggingface.co/apus-ailab/APUS-OpenJev-v1-9B) | 0 | 0 | current |
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| 35B-A3B | — | — | unpublished; excluded |
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| **Series total (project-computed)** | 0 | 0 | complete |
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Totals sum only the published canonical repositories in this snapshot, separately for each time window. Historical snapshots and family/mirror repositories are never added. This is a project-computed summary, not a native Hugging Face Collection counter. Hub download counters are not unique users.
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<!-- OPENJEV-SERIES:END -->
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## 5. 最小推理示例
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根目录 [meta.yaml](meta.yaml) 提供模型版本及配置哈希;下载指定版本时请同时保留它,仓库与原模型路径不变。也可运行 [download_model.py](download_model.py),参数为 `--variant 9B-3000 --local-dir ./APUS-OpenJev-v1`,在同一固定 Hub revision 选择并校验模型。Hub 下载统计按指定元数据文件的请求计数,不等同独立用户或完整权重下载;旧版仅下载子目录的客户端可能绕过此元数据。
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**Authors:** gumpcheng ([https://huggingface.co/xDAN2099](https://huggingface.co/xDAN2099)), zhangxu, [APUS AI-LAB](https://github.com/APUS-AI-Lab)。
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bundle-manifest.json
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},
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"path": "README.md",
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"bytes": 742,
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"sha256": "fdac890892b1e7b80c013e56d3aa75406cba2512b5071268531732d7da7b4e9a"
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"sha256": "f307b3f37691a1ab2624b5769bb32b605ed6893177d73a8631b2180bdd81b4d9"
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],
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"previous_revision": "2e393e5517ffc14fc0b400ec11ba319f42ef28bf",
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"weight_files_changed": false
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}
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}
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{
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"schema": "apus-openjev-series-stats.v1",
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"generated_at": "2026-09-22T03:
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"observed_at": "2026-09-22T03:
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"published_repo_ids": [
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{
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"generated_at": "2026-09-22T03:10:37.132203Z",
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"observed_at": "2026-09-22T03:10:37.132203Z",
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"published_repo_ids": [
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