zhangyiss/mlx-examples
1
0
Fork 0
mirror of https://github.com/ml-explore/mlx-examples.git synced 2025-06-26 10:41:18 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

adding args into dataset handling

Browse Source
This commit is contained in:
Goekdeniz-Guelmez 2025-02-04 10:22:34 +01:00
parent 7173840283
commit bd1a42ec2f
1 changed files with 15 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
llms/mlx_lm/tuner/datasets.py
View File

@ -131,6 +131,7 @@ class CompletionsDataset:
def create_dataset( def create_dataset(
args,
data, data,
tokenizer: PreTrainedTokenizer, tokenizer: PreTrainedTokenizer,
prompt_feature: Optional[str] = None, prompt_feature: Optional[str] = None,
@ -143,7 +144,14 @@ def create_dataset(
if "messages" in sample: if "messages" in sample:
return ChatDataset(data, tokenizer) return ChatDataset(data, tokenizer)
elif "prompt" in sample and "answer" in sample: elif "prompt" in sample and "answer" in sample:
return GRPODataset(data, tokenizer, "prompt", "answer") # Use GRPO Dataset return GRPODataset(
data=data,
tokenizer=tokenizer,
prompt_key="prompt",
answer_key="answer",
use_chat_template=args.use_chat_template,
use_prompt=args.use_prompt
)
elif prompt_feature in sample and completion_feature in sample: elif prompt_feature in sample and completion_feature in sample:
return CompletionsDataset(data, tokenizer, prompt_feature, completion_feature) return CompletionsDataset(data, tokenizer, prompt_feature, completion_feature)
elif "text" in sample: elif "text" in sample:
@ -156,6 +164,7 @@ def create_dataset(
def load_local_dataset( def load_local_dataset(
args,
data_path: Path, data_path: Path,
tokenizer: PreTrainedTokenizer, tokenizer: PreTrainedTokenizer,
prompt_feature: Optional[str] = None, prompt_feature: Optional[str] = None,
@ -166,7 +175,7 @@ def load_local_dataset(
return [] return []
with open(path, "r") as fid: with open(path, "r") as fid:
data = [json.loads(l) for l in fid] data = [json.loads(l) for l in fid]
return create_dataset(data, tokenizer, prompt_feature, completion_feature) return create_dataset(args, data, tokenizer, prompt_feature, completion_feature)
names = ("train", "valid", "test") names = ("train", "valid", "test")
train, valid, test = [load_subset(data_path / f"{n}.jsonl") for n in names] train, valid, test = [load_subset(data_path / f"{n}.jsonl") for n in names]
@ -174,6 +183,7 @@ def load_local_dataset(
def load_hf_dataset( def load_hf_dataset(
args,
data_id: str, data_id: str,
tokenizer: PreTrainedTokenizer, tokenizer: PreTrainedTokenizer,
prompt_feature: Optional[str] = None, prompt_feature: Optional[str] = None,
@ -189,7 +199,7 @@ def load_hf_dataset(
train, valid, test = [ train, valid, test = [
( (
create_dataset( create_dataset(
dataset[n], tokenizer, prompt_feature, completion_feature args, dataset[n], tokenizer, prompt_feature, completion_feature
) )
if n in dataset.keys() if n in dataset.keys()
else [] else []
@ -254,12 +264,12 @@ def load_dataset(args, tokenizer: PreTrainedTokenizer):
completion_feature = getattr(args, "completion_feature", None) completion_feature = getattr(args, "completion_feature", None)
if data_path.exists(): if data_path.exists():
train, valid, test = load_local_dataset( train, valid, test = load_local_dataset(
data_path, tokenizer, prompt_feature, completion_feature args, data_path, tokenizer, prompt_feature, completion_feature
) )
else: else:
print(f"Loading Hugging Face dataset {args.data}.") print(f"Loading Hugging Face dataset {args.data}.")
train, valid, test = load_hf_dataset( train, valid, test = load_hf_dataset(
args.data, tokenizer, prompt_feature, completion_feature args, args.data, tokenizer, prompt_feature, completion_feature
) )
if args.train and len(train) == 0: if args.train and len(train) == 0: