open-cabrita3b-GGUF
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1. **模型用途**: 专门用于手机自动化任务,其他用途效果可能不佳 2. **安全提醒**: 请在受控环境中测试,避免在重要设备上直接使用 3. **性能差异**: 不同量化级别的性能和质量存在差异,请根据需求选择 4. **更新频率**: 模型会根据 Open-AutoGLM 项目更新而更新
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This repository is an immutable public distribution mirror of the exact Q8_0 GGUF used by the native lpminer TensorCash engine. The miner verifies both file size and SHA-256 before using the model. A similarly named GGUF with different bytes is not interchangeable without consensus qualification. This repository mirrors the public artifact distributed at `RacerMiner/Qwen3-8B-Q8_0-full-v2.gguf`. The base Qwen3-8B model and the source repository identify the license as Apache-2.0.
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Qwen0.6B trained on the MetaMathQA dataset using Unsloth. Used to test ExecuTorch LoRA capabilities. These are Qwen3 0.6B models, lowered to XNNPACK, quantized with torchao 8da4w and embedding quantization following the export script in: https://github.com/meta-pytorch/executorch-examples/blob/main/program-data-separation/export_lora.sh See the corresponding README in: https://github.com/meta-pytorch/executorch-examples/tree/main/program-data-separation/cpp/lora_example
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Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
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