Update README.md

README.md

@@ -35,26 +35,22 @@ To use the increased context with KoboldCpp, simply use `--contextsize` to set t
 ## Repositories available
 
 * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-GGML)
+* [4, 5 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-GGML)
 * [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-fp16)
 * [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardLM-13B-V1.1)
 
-<!-- compatibility_ggml start -->
-## Compatibility
+## Prompt template: Vicuna
 
-These GGMLs will work with any llama.cpp-compatible GGML client that supports k-quants.
-
-However the increased context length won't work without specific support. See the note in the introduction for details on using increased context.
+```
+A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
+USER: prompt
+ASSISTANT:
+```
 
-## Explanation of the new k-quant methods
+<!-- compatibility_ggml start -->
+## Compatibility
 
-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.
+Due to the non-standard vocab size of 32001, k-quants are not possible with this model.  Therefore only the 'old' quants can be provided (q4_0, q4_1, q5_0, q5_1, q8_0).
 
 Refer to the Provided Files table below to see what files use which methods, and how.
 <!-- compatibility_ggml end -->