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Jev Boost re6-hover-redocred Embeddings (Qwen3.6-35B-A3B-FP8)
Hugging Face dataset of Jev Decision Index-shaped state/action embeddings built from Re-DocRED. Holds 13,312 train and 1,517 validation pairs embedded with Qwen3.6-35B-A3B-FP8 at 2048 dimensions for post-training CLM heads.
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- Added
- Oct 2026
Highlights
- 13,312 train and 1,517 validation pairs, each paired with one hard negative (k=1).
- Claims of 1-3 annotated facts about one entity, with swapped-object NOT_SUPPORTED variants.
- Embeddings are float16 [n, 2048] from vLLM last-token pooling, L2-normalized.
- Items sharing a paragraph with a Jev suite item are dropped; at most 20,000 rows by hash sample.
- Total file size 190 MB; source text licensed MIT (Re-DocRED) and CC BY-SA (Wikipedia).
Questions about this entry
Short answers drawn from the linked project or page.
It provides state/action embeddings for post-training CLM heads via clm_train.data.jev_boost. Records are converted into decisions shaped like the Jev Decision Index's Retrieval & Classification requests, targeting HoVer claim verification.
Qwen/Qwen3.6-35B-A3B-FP8, embedded with vLLM pooling on the last token and L2-normalized to 2048 dimensions. Metadata records max_model_len 16,384 and state_head_tokens 2,048.
13,312 train pairs and 1,517 validation pairs, each with one hard negative. The build is capped at 20,000 rows by a hash sample, and the Hugging Face viewer shows two rows.
Each record is a claim of 1-3 human-annotated facts about one entity of a Wikipedia lead, marked SUPPORTED, plus the same claim with one fact's object swapped for another entity of the same role the lead names, marked NOT_SUPPORTED, against the lead text.
One-token sequences are never embedded, since vLLM's pooling of a one-token prompt is noise for this model. They are zero here, and single_tokens.npz points them at jackyk02/qwen3.6-35b-a3b-fp8-single-token-embeddings, applied on load by clm_train.data.stores.EmbeddingDir.
The source's license: MIT for Re-DocRED and CC BY-SA for the Wikipedia text. The embedding vectors are a derivative of that text, so the same terms apply to them.
Use clm-train posttrain with the source hf:boost-re6-hover-redocred-qwen3.6. Each split directory contains state_embeddings.pt, action_embeddings.pt, neg_embeddings.pt, neg_counts.pt, single_tokens.npz and metadata.json; total file size is 190 MB.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
X: Standard One SH (StandardThinking)
LinkedIn: What multimodal decision models actually change, and what they do not.
LinkedInJevDirectory.org
Hacker News: Decision Index 0.2.1 scores open reproductions of Jev on a frozen suite.
jackbravo4 pointsDecision Index 0.2.1 – 70 open reproductions of TypeSafe Jev's Decision ModelRead the thread on Hacker NewsHacker News: Pointer to a DiffusionGemma plus vLLM tutorial for a Jev-shaped classifier.
1 pointHow to build a Jev-style classifier with DiffusionGemma and vLLMRead the thread on Hacker NewsMore Jev resources like this
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