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Jev Boost to6 fbtod when2call Qwen3.6-35B-A3B-FP8 Embeddings
A Hugging Face dataset of Qwen3.6-35B-A3B-FP8 state, action, and hard-negative embeddings built from xSID's English alarm, reminder, and weather requests, shaped as When2Call multiple-choice decisions for post-training CLM heads.
- Reading time
- 2 min
- Published by
- Community
- Added
- Oct 2026
Highlights
- 9,525 train and 1,048 validation pairs, each stored with 3 hard negatives
- Embeddings are 2048-d float16, L2-normalized, last-token pooled with vLLM
- States render the benchmark's request plus its instructions; options are never listed
- Rows are capped at 20,000 by hash sample; items sharing a paragraph with a Jev suite item are dropped
- Licensed CC BY-SA 4.0 from the xSID source; the vectors are a derivative of its text
Questions about this entry
Short answers drawn from the linked project or page.
The card documents loading through clm_train, which reads the dataset with clm_train.data.jev_boost and the EmbeddingDir store; the documented command is clm-train posttrain --source hf:boost-to6-fbtod-when2call-qwen3.6. The files are also readable directly with the Hugging Face Datasets library, pandas, or Polars.
Vectors come from Qwen/Qwen3.6-35B-A3B-FP8 using vLLM pooling with the last token, L2-normalized to 2048 dimensions. The metadata records hidden_size 2,048, max_model_len 16,384, state_head_tokens 2,048, and the clm_train.render.Renderer renderer.
The train split of robvanderg/xSID at revision 3002bd48d53376c0234fa2cac5676270fdb88679: Facebook's crowd-written English alarm, reminder, and weather requests with their slots. SNIPS is left out. Requests missing a time, task, or place become request-for-info; complete ones become a call.
Train holds 9,525 pairs and validation 1,048, each with 3 hard negatives stored in neg_embeddings.pt. The card notes at most 8 hard negatives per row are considered, with near-copies of the gold option dropped, and no one-token actions or negatives in either split.
The card states that vLLM's pooling of a one-token prompt is noise for this model, so one-token sequences are never embedded and are zeroed. single_tokens.npz records their token ids and points them at jackyk02/qwen3.6-35b-a3b-fp8-single-token-embeddings, which EmbeddingDir applies on load.
The dataset carries the source's license, CC BY-SA 4.0 for the Facebook multilingual TOD / xSID data, and the card notes no license is stated on the source card itself. The vectors are described as a derivative of that source text, so the same terms are expected to apply.
Each split provides state_embeddings.pt and action_embeddings.pt as float16 [n, 2048], neg_embeddings.pt as float16 [n, k, 2048] zero-padded, neg_counts.pt as int64 [n], single_tokens.npz, and metadata.json with one dict per row. Total file size is 222 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.
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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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