resolving conflicts

UPDATES.md

@@ -0,0 +1,19 @@
+## System Prompt Update
+
+### Observed Issue
+We received feedback from the community on our prompt template and we are providing an update to reduce the false refusal rates seen. False refusals occur when the model incorrectly refuses to answer a question that it should, for example due to overly broad instructions to be cautious in how it provides responses. 
+
+### Updated approach
+Based on evaluation and analysis, we recommend the removal of the system prompt as the default setting.  Pull request [#626](https://github.com/facebookresearch/llama/pull/626) removes the system prompt as the default option, but still provides an example to help enable experimentation for those using it. 
+
+## Token Sanitization Update
+
+### Observed Issue
+The PyTorch scripts currently provided for tokenization and model inference allow for direct prompt injection via string concatenation. Prompt injections allow for the addition of special system and instruction prompt strings from user-provided prompts. 
+
+As noted in the documentation, these strings are required to use the fine-tuned chat models. However, prompt injections have also been used for manipulating or abusing models by bypassing their safeguards, allowing for the creation of content or behaviors otherwise outside the bounds of acceptable use. 
+
+### Updated approach
+We recommend sanitizing [these strings](https://github.com/facebookresearch/llama#fine-tuned-chat-models) from any user provided prompts. Sanitization of user prompts mitigates malicious or accidental abuse of these strings. The provided scripts have been updated to do this. 
+
+Note: even with this update safety classifiers should still be applied to catch unsafe behaviors or content produced by the model. An [example](https://github.com/facebookresearch/llama-recipes/blob/main/inference/inference.py) of how to deploy such a classifier can be found in the llama-recipes repository.

docs/multi_gpu.md

@@ -82,7 +82,7 @@ Currently 4 datasets are supported that can be found in [Datasets config file](.
 * `alpaca_dataset` : to get this open source data please download the `aplaca.json` to `ft_dataset` folder.
 
 ```bash
-wget -P ft_dataset https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json
+wget -P ft_datasets https://raw.githubusercontent.com/tatsu-lab/stanford_alpaca/main/alpaca_data.json
 ```
 
 * `samsum_dataset`

docs/single_gpu.md

@@ -47,7 +47,7 @@ Currently 4 datasets are supported that can be found in [Datasets config file](.
 * `alpaca_dataset` : to get this open source data please download the `aplaca.json` to `ft_dataset` folder.
 
 ```bash
-wget -P ft_dataset https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json
+wget -P ft_datasets https://raw.githubusercontent.com/tatsu-lab/stanford_alpaca/main/alpaca_data.json
 ```
 
 * `samsum_dataset`

ft_datasets/alpaca_dataset.py

@@ -42,6 +42,9 @@ class InstructionDataset(Dataset):
         return len(self.ann)
 
     def __getitem__(self, index):
+        IGNORE_INDEX = -100  # The default setting in CrossEntropyLoss
+
+
         ann = self.ann[index]
         if ann.get("input", "") == "":
             prompt = PROMPT_DICT["prompt_no_input"].format_map(ann)
@@ -66,7 +69,7 @@ class InstructionDataset(Dataset):
         example_mask = example.ge(0)
         label_mask = labels.ge(0)
         example[~example_mask] = 0
-        labels[~label_mask] = 0
+        labels[~label_mask] = IGNORE_INDEX
         example_mask = example_mask.float()
         label_mask = label_mask.float()
 

inference/chat_utils.py

@@ -16,22 +16,11 @@ Dialog = List[Message]
 
 B_INST, E_INST = "[INST]", "[/INST]"
 B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-DEFAULT_SYSTEM_PROMPT = """\
-You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
-
 def format_tokens(dialogs, tokenizer):
     prompt_tokens = []
     for dialog in dialogs:
-        if dialog[0]["role"] != "system":
-                dialog = [
-                    {
-                        "role": "system",
-                        "content": DEFAULT_SYSTEM_PROMPT,
-                    }
-                ] + dialog
-        dialog = [
+        if dialog[0]["role"] == "system":
+            dialog = [
             {
                 "role": dialog[1]["role"],
                 "content": B_SYS
@@ -47,7 +36,7 @@ def format_tokens(dialogs, tokenizer):
             "starting with user and alternating (u/a/u/a/u...)"
         )
         """
-        Please verify that yout tokenizer support adding "[INST]", "[/INST]" to your inputs.
+        Please verify that your tokenizer support adding "[INST]", "[/INST]" to your inputs.
         Here, we are adding it manually.
         """
         dialog_tokens: List[int] = sum(

scripts/spellcheck_conf/wordlist.txt

@@ -1089,4 +1089,6 @@ fragmentations
 intra
 nightlies
 recenly
-uncomment
+uncomment
+Sanitization
+tokenization