--- language: - multilingual tags: - code-generation - transformers - TensorBlock - GGUF license: mit base_model: Kwaipilot/KwaiCoder-23B-A4B-v1 ---
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## Kwaipilot/KwaiCoder-23B-A4B-v1 - GGUF This repo contains GGUF format model files for [Kwaipilot/KwaiCoder-23B-A4B-v1](https://huggingface.co/Kwaipilot/KwaiCoder-23B-A4B-v1). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39). ## Our projects
Awesome MCP Servers TensorBlock Studio
Project A Project B
A comprehensive collection of Model Context Protocol (MCP) servers. A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀 👀 See what we built 👀
## Prompt template ``` Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format. ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [KwaiCoder-23B-A4B-v1-Q2_K.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q2_K.gguf) | Q2_K | 9.475 GB | smallest, significant quality loss - not recommended for most purposes | | [KwaiCoder-23B-A4B-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q3_K_S.gguf) | Q3_K_S | 11.022 GB | very small, high quality loss | | [KwaiCoder-23B-A4B-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q3_K_M.gguf) | Q3_K_M | 12.039 GB | very small, high quality loss | | [KwaiCoder-23B-A4B-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q3_K_L.gguf) | Q3_K_L | 12.547 GB | small, substantial quality loss | | [KwaiCoder-23B-A4B-v1-Q4_0.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q4_0.gguf) | Q4_0 | 13.134 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [KwaiCoder-23B-A4B-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q4_K_S.gguf) | Q4_K_S | 14.089 GB | small, greater quality loss | | [KwaiCoder-23B-A4B-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q4_K_M.gguf) | Q4_K_M | 15.431 GB | medium, balanced quality - recommended | | [KwaiCoder-23B-A4B-v1-Q5_0.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q5_0.gguf) | Q5_0 | 16.023 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [KwaiCoder-23B-A4B-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q5_K_S.gguf) | Q5_K_S | 16.474 GB | large, low quality loss - recommended | | [KwaiCoder-23B-A4B-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q5_K_M.gguf) | Q5_K_M | 17.624 GB | large, very low quality loss - recommended | | [KwaiCoder-23B-A4B-v1-Q6_K.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q6_K.gguf) | Q6_K | 20.841 GB | very large, extremely low quality loss | | [KwaiCoder-23B-A4B-v1-Q8_0.gguf](https://huggingface.co/tensorblock/KwaiCoder-23B-A4B-v1-GGUF/blob/main/KwaiCoder-23B-A4B-v1-Q8_0.gguf) | Q8_0 | 24.724 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/KwaiCoder-23B-A4B-v1-GGUF --include "KwaiCoder-23B-A4B-v1-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/KwaiCoder-23B-A4B-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```