phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-08-03 09:21:45 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

address comments

Browse source
This commit is contained in:
Botao Chen 2024-12-12 14:13:17 -08:00
parent 3378c100f6
commit aad0dedc85
1 changed files with 13 additions and 5 deletions
Download patch file Download diff file Expand all files Collapse all files

18
llama_stack/providers/inline/post_training/torchtune/utils.py
View file

@ -43,6 +43,10 @@ class ModelConfigs(BaseModel):
Llama_3_8B_Instruct: ModelConfig Llama_3_8B_Instruct: ModelConfig
class DatasetSchema(BaseModel):
alpaca: List[Dict[str, ParamType]]
MODEL_CONFIGS = ModelConfigs( MODEL_CONFIGS = ModelConfigs(
Llama3_2_3B_Instruct=ModelConfig( Llama3_2_3B_Instruct=ModelConfig(
model_definition=lora_llama3_2_3b, model_definition=lora_llama3_2_3b,
@ -56,8 +60,9 @@ MODEL_CONFIGS = ModelConfigs(
), ),
) )
EXPECTED_DATASET_SCHEMA: Dict[str, List[Dict[str, ParamType]]] = {
"alpaca": [ EXPECTED_DATASET_SCHEMA = DatasetSchema(
alpaca=[
{ {
ColumnName.instruction.value: StringType(), ColumnName.instruction.value: StringType(),
ColumnName.input.value: StringType(), ColumnName.input.value: StringType(),
@ -74,7 +79,7 @@ EXPECTED_DATASET_SCHEMA: Dict[str, List[Dict[str, ParamType]]] = {
ColumnName.output.value: StringType(), ColumnName.output.value: StringType(),
}, },
] ]
} )
BuildLoraModelCallable = Callable[..., torch.nn.Module] BuildLoraModelCallable = Callable[..., torch.nn.Module]
BuildTokenizerCallable = Callable[..., Llama3Tokenizer] BuildTokenizerCallable = Callable[..., Llama3Tokenizer]
@ -138,7 +143,10 @@ async def validate_input_dataset_schema(
if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0: if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
raise ValueError(f"Dataset {dataset_id} does not have a schema defined.") raise ValueError(f"Dataset {dataset_id} does not have a schema defined.")
if dataset_def.dataset_schema not in EXPECTED_DATASET_SCHEMA[dataset_type]: if not hasattr(EXPECTED_DATASET_SCHEMA, dataset_type):
raise ValueError(f"Dataset type {dataset_type} is not supported.")
if dataset_def.dataset_schema not in getattr(EXPECTED_DATASET_SCHEMA, dataset_type):
raise ValueError( raise ValueError(
f"Dataset {dataset_id} does not have a correct input schema in {EXPECTED_DATASET_SCHEMA[dataset_type]}" f"Dataset {dataset_id} does not have a correct input schema in {EXPECTED_DATASET_SCHEMA.dataset_type}"
) )