Update README.md

README.md

@@ -1,6 +1,13 @@
 ---
 inference: false
-license: other
+license: mit
+language:
+- en
+library_name: transformers
+datasets:
+- psmathur/alpaca_orca
+- psmathur/dolly-v2_orca
+- psmathur/WizardLM_Orca
 ---
 
 <!-- header start -->
@@ -34,6 +41,31 @@ GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/gger
 * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_3B-GGML)
 * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_3b)
 
+## Prompt template: Alpaca with system message
+
+```
+### System:
+You are an AI assistant that follows instruction extremely well. Help as much as you can.
+
+### User:
+prompt
+
+### Response
+```
+or
+```
+### System:
+You are an AI assistant that follows instruction extremely well. Help as much as you can.
+
+### User:
+prompt
+
+### Input
+input
+
+### Response
+```
+
 <!-- compatibility_ggml start -->
 ## Compatibility
 
@@ -45,19 +77,9 @@ These are guaranteed to be compatbile with any UIs, tools and libraries released
 
 ### 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`
 
-These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`.
-
-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.
-
-## Explanation of the new k-quant methods
+These cannot be provided with Open Llama 3B models at this time, due to an issue in llama.cpp.
 
-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 being worked on in the llama.cpp repo.  More issues here: https://github.com/ggerganov/llama.cpp/issues/1919
 
 Refer to the Provided Files table below to see what files use which methods, and how.
 <!-- compatibility_ggml end -->