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AI Models 84760

• Qwen3-4B-Instruct-2507-NVFP4↗

  llmat

  NVFP4-quantized version of `Qwen/Qwen3-4B-Instruct-2507` produced with llmcompressor. - Quantization scheme: NVFP4 (linear layers, `lm_head` excluded) - Calibration samples: 512 - Max sequence length during calibration: 2048 This model can be deployed efficiently using the vLLM backend, as shown in the example below.

• gemma-4-E4B-it-uncensored-heretic↗

  llmfan46

• pos_thai↗

  lunarlist

• GLM-5.2-E2M1-Furnace↗

  m8than

  Complete experimental conversion of zai-org/GLM-5.2-FP8 for Furnace. **142 safetensors shards, 118,629 tensors, approximately 388 GB.** The source checkpoint is unchanged. This is a full target model, not a pilot or draft. Routed experts are E2M1 (4-bit, block-128 scales), as before. Attention, dense and shared-expert projections are now **FP8 block-128, passed through unchanged from the source** (`dense_weight_format: fp8_block128` in `quantization_config`), instead of BF16. Expert weights and their reconstruction error are identical to the previous revision. The previous BF16-dense revision is `16c3f5ca406349306ffe69de7726d7156b09b91b`. This needs a Furnace build that understands `dense_weight_format` (the commit linked above or later). FP8 dense weights let Furnace's `FURNACE_MEGAKERNEL=1` decode path qualify on this checkpoint.

• mvinverse↗

  maddog241

  This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Code: MVInverse Code - Paper: MVInverse Paper - Page: MVInverse Page

• Qwen3.6-27B-Isometry-Terse↗

  maldv

• aelaectra-danish-electra-small-cased↗

  Maltehb

• mergekit-ties-mojzqgu-GGUF↗

  MaziyarPanahi

• T3qT3qm7xp-7B-GGUF↗

  MaziyarPanahi

• wavecoder-ultra-6.7b↗

  Microsoft