Add fine-tuning template for data gen

[scosman]

Summary by CodeRabbit

• New Features

  • Added a new built-in template for generating fine-tuning data, providing detailed instructions for topic, input, and output generation.

• Improvements

  • Updated the label in the guidance selection dropdown from "Built-in Templates for Evals" to "Built-in Templates" for clarity.

• Tests

  • Updated test cases to reflect the new label for built-in templates in the dropdown options.

[coderabbitai]

Walkthrough

The changes introduce a new static template for fine-tuning data generation, update the label for built-in template groups in both code and tests, and modify the generate_fine_tuning_data_for_tag function to pass "fine_tuning" as a guidance type. No exported API signatures or control flow were otherwise altered.

Changes

File(s)	Change Summary
.../data_gen_intro.svelte	Changed on_setup call to pass "fine_tuning" as the second argument instead of null.
.../synth_data_guidance_datamodel.ts	Added a new static template with ID "fine_tuning" for data generation; changed built-in template group label.
.../synth_data_guidance_datamodel.test.ts	Updated test assertions and group label checks from "Built-in Templates for Evals" to "Built-in Templates".

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant UI (data_gen_intro.svelte)
    participant GuidanceDataModel

    User->>UI (data_gen_intro.svelte): Initiate fine-tuning data generation
    UI (data_gen_intro.svelte)->>GuidanceDataModel: on_setup("training", "fine_tuning", ...)
    GuidanceDataModel-->>UI (data_gen_intro.svelte): Use "fine_tuning" static template for guidance
    UI (data_gen_intro.svelte)-->>User: Display generated data guidance

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Possibly related PRs

• Update our topic tree evals and eval gen #409: Refactors data generation tasks and prompts to distinguish between "training" and "eval" types, closely related to the introduction of the "fine_tuning" guidance type and template in this PR.

Poem

In the warren of code, a new path we define,
For fine-tuning guidance, templates align.
With labels refreshed and logic so neat,
Rabbits rejoice—our features complete!
🐇✨

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📥 Commits

Reviewing files that changed from the base of the PR and between 12a7d7c and 6d919fe.

📒 Files selected for processing (3)

• app/web_ui/src/routes/(app)/generate/[project_id]/[task_id]/data_gen_intro.svelte (1 hunks)
• app/web_ui/src/routes/(app)/generate/[project_id]/[task_id]/synth_data_guidance_datamodel.test.ts (2 hunks)
• app/web_ui/src/routes/(app)/generate/[project_id]/[task_id]/synth_data_guidance_datamodel.ts (2 hunks)

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🔇 Additional comments (4)

app/web_ui/src/routes/(app)/generate/[project_id]/[task_id]/data_gen_intro.svelte (1)

147-147: LGTM! Template ID correctly passed for fine-tuning flow.

The change properly passes the "fine_tuning" template ID to align with the new static template added in the datamodel. This ensures the fine-tuning data generation flow uses the appropriate template.

app/web_ui/src/routes/(app)/generate/[project_id]/[task_id]/synth_data_guidance_datamodel.test.ts (1)

564-564: LGTM! Test expectations correctly updated for label change.

The test expectations have been properly updated to reflect the label change from "Built-in Templates for Evals" to "Built-in Templates", maintaining consistency with the datamodel changes.

Also applies to: 587-587, 595-595

app/web_ui/src/routes/(app)/generate/[project_id]/[task_id]/synth_data_guidance_datamodel.ts (2)

508-508: LGTM! Label generalized to reflect broader template usage.

The label change from "Built-in Templates for Evals" to "Built-in Templates" appropriately reflects that templates now serve both evaluation and fine-tuning purposes.

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531-554: Excellent implementation of the fine-tuning template.

The new static template is well-structured and comprehensive:

• Properly follows the StaticTemplates type structure
• Sets suggest_uncensored: false appropriately for fine-tuning use cases
• Provides clear, detailed guidance for each generation phase (topics, inputs, outputs)
• Includes helpful instructions about edge cases and data distribution
• Maintains consistency with existing template patterns

The template content effectively guides users to generate high-quality training data while handling edge cases appropriately.

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