nllb-200-distilled-1.3B-ct2-int8
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This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2777 - Accuracy: 0.8455 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2
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An int4 group-scaled conversion of `zai-org/GLM-5.3-Flash`, packed in the container format that colibrì streams from disk. **This is not the official checkpoint.** The weights are Z.ai's; this repository only changes how they are stored. It will not load in transformers or vLLM. It loads in colibrì's `glm53` engine and nothing else. Running a 321B multimodal model on a machine with 25 GB of RAM, by keeping the routed experts on disk and reading only the ones each token selects.
An int4 group-scaled conversion of `Qwen/Qwen3-Coder-30B-A3B-Instruct`, packed in the container format that colibrì reads. **This is not the official checkpoint.** The weights are the Qwen team's, released under Apache 2.0; this repository only changes how they are stored. It will not load in transformers or vLLM. It loads in colibrì's `qwen36` engine, with Qwen3 MoE support from #1828 onwards. A 30B coding model with 3.3B active parameters per token, running on the CPU of an ordinary machine.
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