llama-v2-chinese-ymcui-alpaca-GGML-13B

LLaMA-v2-chinese-alpaca-13B-GGML (ymcui)

Here are the GGML converted and/or quantized models for ymcui's Chinese LLaMA-v2 Alpaca 13B.

!NOTE! The GGML filetype is outdated. Prefer GGUF format going forward.

Explanation of quantisation methods

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Methods:

• type-0 (Q4_0, Q5_0, Q8_0) - weights w are obtained from quants q using w = d * q, where d is the block scale.
• type-1 (Q4_1, Q5_1) - weights are given by w = d * q + m, where m is the block minimum

The new methods available are:

• GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
• GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
• GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
• GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
• GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
• GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.

This is exposed via llama.cpp quantization types that define various "quantization mixes" as follows:

• LLAMA_FTYPE_MOSTLY_Q2_K - uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
• LLAMA_FTYPE_MOSTLY_Q3_K_S - uses GGML_TYPE_Q3_K for all tensors
• LLAMA_FTYPE_MOSTLY_Q3_K_M - uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
• LLAMA_FTYPE_MOSTLY_Q3_K_L - uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
• LLAMA_FTYPE_MOSTLY_Q4_K_S - uses GGML_TYPE_Q4_K for all tensors
• LLAMA_FTYPE_MOSTLY_Q4_K_M - uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
• LLAMA_FTYPE_MOSTLY_Q5_K_S - uses GGML_TYPE_Q5_K for all tensors
• LLAMA_FTYPE_MOSTLY_Q5_K_M - uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
• LLAMA_FTYPE_MOSTLY_Q6_K- uses 6-bit quantization (GGML_TYPE_Q8_K) for all tensors

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Provided files

Model Sources

• Repository: [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2]