Update README.md

README.md

@@ -31,7 +31,7 @@ GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/gger
 ## Repositories available
 
 * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GGML)
+* [4, 5, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1.1-GGML)
 * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardLM-13B-V1.1)
 
 <!-- compatibility_ggml start -->
@@ -43,23 +43,14 @@ I have quantized these 'original' quantisation methods using an older version of
 
 These are guaranteed to be compatbile with any UIs, tools and libraries released since late May.
 
-### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
+### New k-quant methods: not supported at the moment due to model's vocab size
 
-These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`.
+Unfortunately it is not possible to make the new k-quant format quantisations for this model at this time.
 
-They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt.
+This is because the model uses a non-standard vocab size of 32,001, which is not divisible by 256.
 
-## Explanation of the new k-quant methods
+This is being investigated by the llama.cpp team and may be fixed in future. You can read more about that here: https://github.com/ggerganov/llama.cpp/issues/1919
 
-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.
-
-Refer to the Provided Files table below to see what files use which methods, and how.
 <!-- compatibility_ggml end -->
 
 ## Provided files