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added regenerated data processing for llama series #396

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Merged
FrankLeeeee merged 1 commit into from
Dec 28, 2025
Merged

added regenerated data processing for llama series #396

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50 changes: 42 additions & 8 deletions scripts/prepare_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,10 @@ def parse_args():
"sharegpt",
"eaglechat",
"perfectblend",
"perfectblend-llama3.1-8b-instruct",
"perfectblend-llama3.3-70b-instruct",
"perfectblend-llama4-scout-instruct",
"perfectblend-llama4-maverick-instruct",
"magpie-qwen2.5-pro-1m-v0.1",
"sharegpt4v",
"allava4v",
Expand Down Expand Up @@ -189,20 +193,26 @@ def process_and_save_ds(train_ds, test_ds, output_path, proc_fn, dataset_name):
total_skipped_count = 0
with open(train_output_jsonl_path, "w") as f:
for item in tqdm(train_ds, desc=f"Processing {dataset_name} dataset"):
row, skipped_count = proc_fn(item)
if row is None:
continue
total_skipped_count += skipped_count
if proc_fn is not None:
row, skipped_count = proc_fn(item)
if row is None:
continue
total_skipped_count += skipped_count
else:
row = item
f.write(json.dumps(row, ensure_ascii=False) + "\n")

if test_ds is not None:
test_output_jsonl_path = output_path.joinpath(f"{dataset_name}_test.jsonl")
with open(test_output_jsonl_path, "w") as f:
for item in tqdm(test_ds, desc=f"Processing {dataset_name} test dataset"):
row, skipped_count = proc_fn(item)
if row is None:
continue
total_skipped_count += skipped_count
if proc_fn is not None:
row, skipped_count = proc_fn(item)
if row is None:
continue
total_skipped_count += skipped_count
else:
row = item
f.write(json.dumps(row, ensure_ascii=False) + "\n")
Comment on lines 194 to 216
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@gemini-code-assist gemini-code-assist bot Dec 27, 2025

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medium

There is significant code duplication between the processing loops for the training dataset (lines 195-203) and the test dataset (lines 208-216). This makes the code harder to maintain, as any changes would need to be applied in two places.

To improve this, you could extract the common logic into a helper function. This function would take a dataset, a file handle, and the processing function (proc_fn) as arguments, and would contain the loop and writing logic. This would make process_and_save_ds cleaner and more maintainable.


if total_skipped_count > 0:
Expand Down Expand Up @@ -252,6 +262,30 @@ def main():
ds = load_dataset("mlabonne/open-perfectblend")["train"]
ds = ds.map(add_index, with_indices=True)
proc_fn = process_sharegpt_row
elif args.dataset == "perfectblend-llama3.1-8b-instruct":
ds = load_dataset("frankleeeee/PerfectBlend-Regenerated-Llama-3.1-8B-Instruct")[
"train"
]
ds = ds.map(add_index, with_indices=True)
proc_fn = None
elif args.dataset == "perfectblend-llama3.3-70b-instruct":
ds = load_dataset(
"frankleeeee/PerfectBlend-Regenerated-Llama-3.3-70B-Instruct"
)["train"]
ds = ds.map(add_index, with_indices=True)
proc_fn = None
elif args.dataset == "perfectblend-llama4-scout-instruct":
ds = load_dataset(
"frankleeeee/PerfectBlend-Regenerated-Llama-4-Scout-17B-16E-Instruct"
)["train"]
ds = ds.map(add_index, with_indices=True)
proc_fn = None
elif args.dataset == "perfectblend-llama4-maverick-instruct":
ds = load_dataset(
"frankleeeee/PerfectBlend-Regenerated-Llama-4-Maverick-17B-128E-Instruct"
)["train"]
ds = ds.map(add_index, with_indices=True)
proc_fn = None
Comment on lines +265 to +288
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@gemini-code-assist gemini-code-assist bot Dec 27, 2025

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medium

The elif chain for handling the new perfectblend-llama* datasets contains a lot of repetitive code. Each block loads a dataset from a specific Hugging Face repository, maps add_index, and sets proc_fn to None.

To make this more maintainable and easier to extend, you could use a dictionary to map the dataset names to their repository IDs. This would consolidate the logic into a single block.

Suggested change
elif args.dataset == "perfectblend-llama3.1-8b-instruct":
ds = load_dataset("frankleeeee/PerfectBlend-Regenerated-Llama-3.1-8B-Instruct")[
"train"
]
ds = ds.map(add_index, with_indices=True)
proc_fn = None
elif args.dataset == "perfectblend-llama3.3-70b-instruct":
ds = load_dataset(
"frankleeeee/PerfectBlend-Regenerated-Llama-3.3-70B-Instruct"
)["train"]
ds = ds.map(add_index, with_indices=True)
proc_fn = None
elif args.dataset == "perfectblend-llama4-scout-instruct":
ds = load_dataset(
"frankleeeee/PerfectBlend-Regenerated-Llama-4-Scout-17B-16E-Instruct"
)["train"]
ds = ds.map(add_index, with_indices=True)
proc_fn = None
elif args.dataset == "perfectblend-llama4-maverick-instruct":
ds = load_dataset(
"frankleeeee/PerfectBlend-Regenerated-Llama-4-Maverick-17B-128E-Instruct"
)["train"]
ds = ds.map(add_index, with_indices=True)
proc_fn = None
elif args.dataset in [
"perfectblend-llama3.1-8b-instruct",
"perfectblend-llama3.3-70b-instruct",
"perfectblend-llama4-scout-instruct",
"perfectblend-llama4-maverick-instruct",
]:
dataset_map = {
"perfectblend-llama3.1-8b-instruct": "frankleeeee/PerfectBlend-Regenerated-Llama-3.1-8B-Instruct",
"perfectblend-llama3.3-70b-instruct": "frankleeeee/PerfectBlend-Regenerated-Llama-3.3-70B-Instruct",
"perfectblend-llama4-scout-instruct": "frankleeeee/PerfectBlend-Regenerated-Llama-4-Scout-17B-16E-Instruct",
"perfectblend-llama4-maverick-instruct": "frankleeeee/PerfectBlend-Regenerated-Llama-4-Maverick-17B-128E-Instruct",
}
repo_id = dataset_map[args.dataset]
ds = load_dataset(repo_id)["train"]
ds = ds.map(add_index, with_indices=True)
proc_fn = None

elif args.dataset == "magpie-qwen2.5-pro-1m-v0.1":
ds = load_dataset("Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1")["train"]
ds = ds.rename_column("uuid", "id")
Expand Down