Jeopardy bot! (#17)

* support for jeopardy dataset

* commit the final config for jeopardy bot

configs/llama_7B_jeopardy.yml

@@ -0,0 +1,58 @@
+base_model: huggyllama/llama-7b
+base_model_config: huggyllama/llama-7b
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+load_in_8bit: false
+datasets:
+  - path: openaccess-ai-collective/jeopardy
+    type: jeopardy
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+adapter:
+lora_model_dir:
+sequence_len: 2048
+max_packed_sequence_len: 2048
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+  - q_proj
+  - v_proj
+lora_fan_in_fan_out: false
+wandb_project: jeopardy-bot-7b
+wandb_watch:
+wandb_run_id:
+wandb_log_model: checkpoint
+output_dir: ./jeopardy-bot-7b
+batch_size: 4
+micro_batch_size: 1
+num_epochs: 2
+optimizer: adamw_bnb_8bit
+torchdistx_path:
+lr_scheduler: cosine
+learning_rate: 0.0000002
+train_on_inputs: false
+group_by_length: false
+bf16: true
+tf32: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 5
+xformers_attention: true
+flash_attention:
+gptq_groupsize:
+gptq_model_v1:
+warmup_steps: 20
+eval_steps: 110
+save_steps: 660
+debug:
+deepspeed:
+weight_decay: 0.0001
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "[PAD]"
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

src/axolotl/prompt_tokenizers.py

@@ -89,6 +89,15 @@ class AlpacaPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
         )
 
 
+class JeopardyPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    def parse_instruction_fields(self, prompt) -> (str, str, str):
+        return (
+            prompt["question"],
+            prompt["category"],
+            "what is " + prompt["answer"],
+        )
+
+
 class OpenAssistantPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
     def parse_instruction_fields(self, prompt) -> (str, str, str):
         return (

src/axolotl/prompters.py

@@ -31,6 +31,10 @@ class AlpacaPrompter:
         return output.split(self.response_split)[1].strip()
 
 
+class JeopardyPrompter(AlpacaPrompter):
+    prompt_input = "Below is a Jeopardy clue paired with input providing the category of the clue. Write a concise response that best answers tbe clue given the category.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
+
+
 class GPTeacherPrompter(AlpacaPrompter):
     ...
 

src/axolotl/utils/data.py

@@ -11,13 +11,13 @@ from axolotl.prompt_tokenizers import (
     GPTeacherPromptTokenizingStrategy,
     OpenAssistantPromptTokenizingStrategy,
     AlpacaReflectionPTStrategy,
-    ShareGPTPromptTokenizingStrategy,
+    ShareGPTPromptTokenizingStrategy, JeopardyPromptTokenizingStrategy,
 )
 from axolotl.prompters import (
     AlpacaPrompter,
     GPTeacherPrompter,
     ReflectAlpacaPrompter,
-    ShareGPTPrompter,
+    ShareGPTPrompter, JeopardyPrompter,
 )
 
 
@@ -82,6 +82,12 @@ def load_prepare_datasets(tokenizer, cfg, default_dataset_prepared_path):
                 )
                 ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
                 datasets.append(ds_wrapper)
+            if d.type == "jeopardy":
+                ds_strategy = JeopardyPromptTokenizingStrategy(
+                    JeopardyPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
+                )
+                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
+                datasets.append(ds_wrapper)
             elif d.type == "oasst":
                 ds_strategy = OpenAssistantPromptTokenizingStrategy(
                     AlpacaPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len