Anima-2.9B
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Paths in this model repository match paths relative to `learning/policies/` in the code repository. The top-level domains are `coffee_box`, `coffee_drawer`, `kitchen`, and `nut_assembly`; the original task, algorithm, training configuration, and seed subfolders are preserved. Only model checkpoint ZIP files are included: `final_model.zip`, `best_model.zip`, and `recent_model.zip`. Training/evaluation logs, replay buffers, and executor files are not included.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
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The abliterated (refusal-removed) build of Z.ai's GLM-5.3-Flash — baked directly into the official block-FP8 checkpoint, byte-for-byte drop-in for the original > The **abliterated** (refusal-removed) build of `zai-org/GLM-5.3-Flash` > — a **320B / 18B-active** Mixture-of-Experts model with hybrid linear + sparse attention, > 4-wide **Manifold-Constrained Hyper-Connections**, a native **vision + video** tower, an **MTP** > speculative head, and a **1M-token** context. > > The refusal direction is baked **directly into the official block-FP8 shards** — same format, same > shard layout, same `model.safetensors.index.json`. Every tensor matches the base checkpoint in > name, dtype and shape, so this is a **drop-in replacement** for `zai-org/GLM-5.3-Flash` in any > stack that already serves it. > > **On the `-FP8` name:** block-FP8 *is* the format Z.ai ships GLM-5.3-Flash in — the suffix names the > checkpoint's own precision, not a quantization step we applied. There is no upstream BF16 release to > derive from, so this is the full-precision source as published. This model has had its **safety alignment substantially removed** via *abliteration* (orthogonalizing the refusal direction out of the residual stream). As a direct consequence:
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
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This model is trained on **the mix of full-text and splitted sentences** of `answer`s from Hello-SimpleAI/HC3. More details refer to arxiv: 2301.07597 and Gtihub project Hello-SimpleAI/chatgpt-comparison-detection. The base checkpoint is roberta-base. We train it with all Hello-SimpleAI/HC3 data (without held-out) for 1 epoch.
This model is trained on **the mix of full-text and splitted sentences** of `answer`s from Hello-SimpleAI/HC3-Chinese. More details refer to arxiv: 2301.07597 and Gtihub project Hello-SimpleAI/chatgpt-comparison-detection. The base checkpoint is hfl/chinese-roberta-wwm-ext. We train it with all Hello-SimpleAI/HC3-Chinese data (without held-out) for 2 epochs.
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