Update README.md

README.md

@@ -4,7 +4,7 @@ license: other
 
 # OpenAssistant LLaMa 30B SFT 6
 
-Due to the license attached to LLaMa models by Meta AI it is not possible to directly distribute LLaMa-based models. Instead we provide XOR weights for the OA models.
+Due to the license attached to LLaMA models by Meta AI it is not possible to directly distribute LLaMA-based models. Instead we provide XOR weights for the OA models.
 
 Thanks to Mick for writing the `xor_codec.py` script which enables this process
 
@@ -14,9 +14,9 @@ Note: This process applies to `oasst-sft-6-llama-30b` model. The same process ca
 
 **This process is tested only on Linux (specifically Ubuntu). Some users have reported that the process does not work on Windows. We recommend using WSL if you only have a Windows machine.**
 
-To use OpenAssistant LLaMa-Based Models, you need to have a copy of the original LLaMa model weights and add them to a `llama` subdirectory here.
+To use OpenAssistant LLaMA-Based Models, you need to have a copy of the original LLaMA model weights and add them to a `llama` subdirectory here.
 
-Ensure your LLaMa 30B checkpoint matches the correct md5sums:
+Ensure your LLaMA 30B checkpoint matches the correct md5sums:
 
 ```
 f856e9d99c30855d6ead4d00cc3a5573  consolidated.00.pth
@@ -26,7 +26,9 @@ ea0405cdb5bc638fee12de614f729ebc  consolidated.03.pth
 4babdbd05b8923226a9e9622492054b6  params.json
 ```
 
-**Important: Follow these exact steps to convert your original LLaMa checkpoint to a HuggingFace Transformers-compatible format. If you use the wrong versions of any dependency, you risk ending up with weights which are not compatible with the XOR files.**
+*If you do not have a copy of the original LLaMA weights and cannot obtain one, you may still be able to complete this process. Some users have reported that [this model](https://huggingface.co/elinas/llama-30b-hf-transformers-4.29) can be used as a base for the XOR conversion. This will also allow you to skip to Step 7. However, we only support conversion starting from LLaMA original checkpoint and cannot provide support if you experience issues with this alternative approach.*
+
+**Important: Follow these exact steps to convert your original LLaMA checkpoint to a HuggingFace Transformers-compatible format. If you use the wrong versions of any dependency, you risk ending up with weights which are not compatible with the XOR files.**
 
 1. Create a clean Python **3.10** virtual environment & activate it:
 
@@ -104,9 +106,9 @@ edd1a5897748864768b1fab645b31491  ./tokenizer_config.json
 5cfcb78b908ffa02e681cce69dbe4303  ./pytorch_model-00002-of-00007.bin
 ```
 
-**Important: You should now have the correct LLaMa weights and be ready to apply the XORs. If the checksums above do not match yours, there is a problem.**
+**Important: You should now have the correct LLaMA weights and be ready to apply the XORs. If the checksums above do not match yours, there is a problem.**
 
-7. Once you have LLaMa weights in the correct format, you can apply the XOR decoding:
+7. Once you have LLaMA weights in the correct format, you can apply the XOR decoding:
 
 ```
 python xor_codec.py oasst-sft-6-llama-30b/ oasst-sft-6-llama-30b-xor/oasst-sft-6-llama-30b-xor/ llama30b_hf/