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LongCat-Next

meituan-longcat

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> [!TIP] > **There is a newer version of this model:** > gemma-4-31B-it-qat-NVFP4-mse-Blackwell. > Same base model and calibration set, but with MSE-based weight scaling > instead of absmax, which keeps outliers from crushing the rest of each block. > This repo stays as it is so existing deployments don't change under their feet. Gemma 4 31B IT quantized to NVFP4 for native FP4 inference on NVIDIA Blackwell GPUs. Quantized from Google's unquantized QAT weights (`google/gemma-4-31B-it-qat-q4_0-unquantized`) with NVIDIA Model Optimizer (`modelopt` 0.44.0), default absmax scaling. - **Base Model:** google/gemma-4-31B-it-qat-q4_0-unquantized - **Quantization Format:** `NVFP4` (native FP4 on Blackwell) - **Group Size:** 16 (per-group scaling) - **Excluded Modules:** `lm_head`, `model.embed_vision*`, `model.vision_tower*` (kept in high precision) - **KV cache scales:** none in the checkpoint; use `--kv-cache-dtype fp8` and let vLLM scale dynamically - **Producer:** NVIDIA Model Optimizer (`modelopt` 0.44.0)

Text generation
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ppe-detection

melihuzunoglu

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Object detection
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This model is a fine-tuned version of motheecreator/Deepfake-audio-detection on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0141 - Accuracy: 0.9973 The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5

Audio processing
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**MemT-1.0 is a new generation of memory foundation models that makes persistent, dynamically evolving memory an intrinsic part of model computation.** MemT-1.0 integrates dynamic local memory and trainable hyper memory to retain and leverage historical information without accessing original context at inference. Its persistent state remains constant in size, reducing storage by approximately 66× versus full-context KV cache at 128K history length. * **Memory architecture**. Introduce MemT-1.0 with improved memory storage and utilization, and Mixture-of-Storage (MoS) for scalable memory capacity.

Text generation
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MemT-1.0-Nano

MemTensor

**MemT-1.0 is a new generation of memory foundation models that makes persistent, dynamically evolving memory an intrinsic part of model computation.** MemT-1.0 integrates dynamic local memory and trainable hyper memory to retain and leverage historical information without accessing original context at inference. Its persistent state remains constant in size, reducing storage by approximately 66× versus full-context KV cache at 128K history length. * **Memory architecture**. Introduce MemT-1.0 with improved memory storage and utilization, and Mixture-of-Storage (MoS) for scalable memory capacity.

Text generation
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