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dave8172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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View details ↗DISCOVER & COMPARE
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MeaningBERT
davebulavalMeaningBERT is an automatic and trainable metric for assessing meaning preservation between sentences. MeaningBERT was proposed in our article MeaningBERT: assessing meaning preservation between sentences. Its goal is to assess meaning preservation between two sentences that correlate highly with human judgments and sanity checks. For more details, refer to our publicly available article. > This public version of our model uses the best model trained (where in our article, we present the performance results > of an average of 10 models) for a more extended period (500 epochs instead of 250). We have observed later that the > model can further reduce dev loss and increase performance. Also, we have changed the data augmentation technique used > in the article for a more robust one, that also includes the commutative property of the meaning function. Namely, Meaning(Sent_a, Sent_b) = Meaning(Sent_b, Sent_a). The repository root holds the **v1** model described in the article, unchanged, so anything already using `from_pretrained("davebulaval/MeaningBERT")` keeps returning exactly the scores it returned before.
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View details ↗Global-Innovation-Build-Challenge-V2
davemaxuellkr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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View details ↗scglm-v2-46m
davemaxuellkr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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View details ↗pocket-tts-ptts
davencyw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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View details ↗halogen-qwen3.8-flash-next-uncensored
davenetdev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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View details ↗NudtyFix
Daverrrr75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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View details ↗Qwen_SNOFS
Daverrrr75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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View details ↗INT8 `W8A16` quantization of Blackfrost-AI/Qwen3.8-27B-ABLITERATED-BF16, which is itself an abliterated derivative of `Qwen/Qwen3.8-27B`. > **On this hardware int8 is SLOWER than BF16** — roughly half the decode rate — > and MTP speculative decoding does not work. Both are measured below. Use these > only if you need the smaller memory footprint. Weights 8-bit, **group size 128**, symmetric. Activations are **not** quantized (`pack-quantized`), so this is weight-only int8.
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View details ↗A **W4A16 (AWQ)** quantization of `Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16`, an abliterated ("derisked") variant of Qwen's Qwen3.8-Flash-Next. Multimodal (image-text-to-text), MTP speculative-decode head and PLE n-gram table preserved. Built to run the 80B-class Flash-Next MoE on 2× AMD MI210 (gfx90a) at long context. - **Format:** `compressed-tensors`, **W4A16** — 4-bit weights, group size **128**, symmetric, no zero-point (loads on vLLM's `TritonW4A16` + `fused_moe_kernel_gptq_awq` fast paths). - **Method:** **AWQ** (activation-aware). Smoothing is restricted to the mathematically-exact `up_proj → down_proj` pairs. The standard `norm → q/k/v` and `norm → gate/up` smoothings are deliberately **excluded**: this architecture uses hyper-connections with a data-dependent (sigmoid) gate, so folding a smoothing scale into the norm would change the gate non-linearly and corrupt the weights. - **Quantized:** routed experts (gate/up/down) and full-attention `q/k/v/o`. - **Kept in bf16** (matching the reference recipe): the GDN linear-attention layers (recurrent state — error compounds along the sequence), the shared expert, the QSA indexer, the router gates, hyper-connections, norms, `embed_tokens`, `lm_head`, the **MTP** speculative-decode head, and the **PLE** n-gram embedding table.
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View details ↗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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View details ↗whale-call-detector
davethaler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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View details ↗vjepa2.1-vitb-fpc64-384
davevanveen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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View details ↗EgoX
DAVIAN-Robotics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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View details ↗pi05_droid_jointpos
DAVIAN-Robotics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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View details ↗ACE-Step-1.5-Naija-Legacy-Rhythms-LoRA-v1
David-A-Amoo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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View details ↗my-fusion-v1
David-kim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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View details ↗llama-7b-hf-20230407
David003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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View details ↗Two versions of Command-R-01 35B using TWO new DARK HORROR Neo Imatrix datasets bring an INTENSE horror upgrade to Command-R 01 35B. I created the HORROR datasets using Grand Horror 16B using 90+ different horror prompts for generation. This process created an incredibility intense, visceral and vivid horror stories in an ultra compact form - perfect for an Imatrix NEO class dataset. The dataset is focused exclusively on visceral, vivid horror based on phycological horror. (rather than "gore" or "body part horror"). There is some swearing the dataset, so this might appear in generation.
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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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