Instructions to use litert-community/MiniCPM5-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/MiniCPM5-1B with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
add gpu optimized wi4b32_wi8_afp32 model
#10
by CarreyZhan - opened
- README.md +1 -0
- minicpm_wi4b32_wi8_afp32_gpu_opt.litertlm +3 -0
README.md
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@@ -25,6 +25,7 @@ This repository hosts the [**LiteRT-LM**](https://ai.google.dev/edge/litert-lm)
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## Available Models
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* **`minicpm_wi4b32_wi8_afp32.litertlm`**: This model features mixed INT4-block32(linear)/INT8(embed and lmhead) quantization (wi4b32_wi8) with FP32 activations (afp32).
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* **`MiniCPM5-1B_dynamic_wi8_afp32.litertlm`**: This model features dynamic weight-only INT8 quantization (wi8) with FP32 activations (afp32). It is configured with static prefill memory allocation.
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## Quantization Benchmark
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## Available Models
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* **`minicpm_wi4b32_wi8_afp32.litertlm`**: This model features mixed INT4-block32(linear)/INT8(embed and lmhead) quantization (wi4b32_wi8) with FP32 activations (afp32).
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* **`minicpm_wi4b32_wi8_afp32_gpu_opt.litertlm`**: This model uses same quantization recipe with minicpm_wi4b32_wi8_afp32 but optimized for GPU execution.
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* **`MiniCPM5-1B_dynamic_wi8_afp32.litertlm`**: This model features dynamic weight-only INT8 quantization (wi8) with FP32 activations (afp32). It is configured with static prefill memory allocation.
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## Quantization Benchmark
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minicpm_wi4b32_wi8_afp32_gpu_opt.litertlm
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version https://git-lfs.github.com/spec/v1
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oid sha256:4e15a7cc735ae36e9888d341d44bd53c0579153d7a3379aaf958620eea4816e3
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size 793034752
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