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Running on Zero
Running on Zero
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7f9dfed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | from __future__ import annotations
import gradio as gr
from training.evaluation import (
attach_perplexity,
compare_base_vs_tuned,
default_prompt_cases,
evaluate_responses,
log_eval_report,
)
from training.lora_trainer import build_lora_training_request, vision_finetuning_plan
from training.planner import build_training_plan
from ui.progress import CLICK_PROGRESS
def build_train_tab() -> None:
gr.Markdown("LoRA training is planned for the next implementation slice.")
build_lora_plan_panel()
build_vision_finetuning_panel()
build_local_evaluation_panel()
def build_lora_plan_panel() -> None:
with gr.Row():
rank = gr.Slider(4, 64, value=16, step=4, label="LoRA rank")
epochs = gr.Slider(1, 5, value=1, step=1, label="Epochs")
dataset = gr.Textbox(label="Training dataset", placeholder="data/field_notes.jsonl")
start = gr.Button("Prepare training plan", variant="primary")
output = gr.Textbox(label="Plan", lines=8)
lora_request = gr.JSON(label="LoRA trainer request")
start.click(
plan_training,
[rank, epochs, dataset],
output,
show_progress=CLICK_PROGRESS,
)
lora = gr.Button("Prepare LoRA trainer request")
lora.click(
plan_lora_request,
[rank, epochs, dataset],
lora_request,
show_progress=CLICK_PROGRESS,
)
def build_vision_finetuning_panel() -> None:
gr.Markdown("### Vision fine-tuning")
gr.JSON(vision_finetuning_plan(), label="SWIFT / LLaMA-Factory plan")
def build_local_evaluation_panel() -> None:
gr.Markdown("### Local evaluation")
base_responses = gr.Textbox(
label="Base responses",
lines=4,
placeholder="One response per default prompt case",
)
tuned_responses = gr.Textbox(
label="Tuned responses",
lines=4,
placeholder="One response per default prompt case",
)
tuned_losses = gr.Textbox(
label="Optional tuned losses",
lines=2,
placeholder="Optional negative log likelihood values, comma or newline separated",
)
run_eval = gr.Button("Run local evaluation")
eval_summary = gr.JSON(label="Evaluation summary")
eval_table = gr.Dataframe(
headers=["prompt", "expected", "actual", "exact_match", "notes"],
label="Tuned qualitative table",
interactive=False,
)
run_eval.click(
evaluate_local,
[base_responses, tuned_responses, tuned_losses],
[eval_summary, eval_table],
show_progress=CLICK_PROGRESS,
)
def plan_training(rank_value: int, epoch_value: int, dataset_path: str) -> str:
plan = build_training_plan(
dataset_path=dataset_path,
rank=rank_value,
epochs=epoch_value,
)
return plan.as_text()
def plan_lora_request(rank_value: int, epoch_value: int, dataset_path: str) -> dict:
request = build_lora_training_request(
model_id="minicpm5_1b",
dataset_path=dataset_path,
rank=rank_value,
epochs=epoch_value,
)
return request.as_dict()
def evaluate_local(
base_text: str,
tuned_text: str,
loss_text: str,
) -> tuple[dict, list[list[str]]]:
cases = default_prompt_cases()
base_report = evaluate_responses(cases, base_text.splitlines())
tuned_report = evaluate_responses(cases, tuned_text.splitlines())
tuned_report = attach_perplexity(tuned_report, parse_losses(loss_text))
comparison = compare_base_vs_tuned(base_report, tuned_report)
log_eval_report(tuned_report)
summary = comparison.as_dict()
summary["tuned_perplexity"] = tuned_report.perplexity
return summary, tuned_report.as_table()
def parse_losses(loss_text: str) -> list[float]:
cleaned = loss_text.replace(",", "\n")
return [float(value.strip()) for value in cleaned.splitlines() if value.strip()]
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