feat: add Metharme prompt strategy (#446)

* Add Metharme tokenizing strategy

This strategy accounts for how the Metharme JSONLs are formatted as well as adds duplicated EOS tokens which can help trim model output length.
I haven't gotten the chance to test this yet, and probably won't have the chance for quite a bit, so I'm committing this now.

* Redo Metharme tokenizing strategy

lol

* fix: oops

* Rearrange a conditional

* chore: reformat code in accordance with linter

* chore: Make lint not freak out

* chore: fix lint

---------

Co-authored-by: NanoCode012 <[email protected]>

README.md

@@ -257,6 +257,10 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"conversations": [{"role": "...", "value": "..."}]}
   ```
+- `metharme`: instruction, adds additional eos tokens
+  ```json
+  {"prompt": "...", "generation": "..."}
+  ```
 - `sharegpt_simple.load_role`: conversations where `role` is used instead of `from`
   ```json
   {"conversations": [{"role": "...", "value": "..."}]}

src/axolotl/prompt_strategies/metharme.py

@@ -0,0 +1,76 @@
+"""Module containing the MetharmenPromptTokenizingStrategy and MetharmePrompter class"""
+
+import logging
+from typing import Tuple
+
+from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy
+from axolotl.prompters import AlpacaPrompter
+
+LOG = logging.getLogger("axolotl")
+
+IGNORE_TOKEN_ID = -100
+
+# pylint: disable=duplicate-code
+
+
+class MetharmePromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    """
+    Tokenizing strategy for the Metharme models
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+        return (prompt["prompt"], "", prompt["generation"])
+
+    def _tokenize(
+        self,
+        prompt: str,
+        add_eos_token: bool = True,
+        strip_bos_token: bool = False,
+        num_eos_tokens: int = 3,
+    ):
+        result = self.tokenizer(
+            prompt,
+            truncation=True,
+            max_length=self.sequence_len,
+            padding=False,
+            return_tensors=None,
+        )
+        if len(result["input_ids"]) == 0:
+            LOG.warning("Tokenizer result is empty. You may want to audit your dataset")
+        # If there's already an EOS token there, subtract from the number added
+        if result["input_ids"][-1] == self.tokenizer.eos_token_id:
+            num_eos_tokens -= 1
+
+        if num_eos_tokens > 0 and add_eos_token and len(result["input_ids"]) > 0:
+            for _ in range(num_eos_tokens):
+                if len(result["input_ids"]) < self.sequence_len:
+                    result["input_ids"].append(self.tokenizer.eos_token_id)
+                    result["attention_mask"].append(1)
+
+        if result["input_ids"][0] == self.tokenizer.bos_token_id and strip_bos_token:
+            result["input_ids"] = result["input_ids"][1:]
+            result["attention_mask"] = result["attention_mask"][1:]
+
+        result["labels"] = result["input_ids"].copy()
+        return result
+
+
+class MetharmePrompter(AlpacaPrompter):
+    """
+    Prompter for the Metharme models.
+    """
+
+    system_prompt = ""
+    system_no_input_prompt = ""
+    system_format = ""
+    turn_format = "{instruction}"
+    turn_no_input_format = "{instruction}"
+
+    def __init__(self, *args, **kwargs):  # pylint: disable=super-init-not-called
+        pass
+
+
+def load(tokenizer, cfg):
+    return MetharmePromptTokenizingStrategy(
+        MetharmePrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
+    )