Remove obsolete 200GB target policy

dataset_guide/TARGET_200GB_POLICY.md

@@ -1,95 +0,0 @@
-# 200GB Dataset Target Policy
-
-## Target
-
-The final public dataset target is 200GB total, packaged as ten balanced
-20GB-class checkpoints.
-
-```text
-dataset/
-  checkpoint_YYYYMMDD_HHMMSS_bundleNN_20g/
-    dataset/
-      checkpoint_YYYYMMDD_HHMMSS_bundleNN_20g.jsonl
-```
-
-Each checkpoint folder must contain exactly one dataset JSONL file. Tokenizer,
-Dense, MoE, generation notes, reports, and checksums live outside checkpoint
-folders.
-
-## Current Contract
-
-- Global duplicate policy: exact `text` duplicate count must be zero across the
-  curated stream.
-- Bundle duplicate policy: in-bundle duplicate count must be zero.
-- Format: JSONL only.
-- Required field: non-empty `text`.
-- Preferred metadata: `domain`, `difficulty`, `meta.lang`, `meta.source`,
-  repository/file context when available.
-
-## Quality Order
-
-Use this order when deciding what enters the 200GB target:
-
-1. Real public code FIM from The Stack v2 family already staged in
-   `data/v2_shards`.
-2. Verified high-quality Grok/Codex synthetic FIM and code completion records.
-3. Deduped synthetic `code_gen` records with useful executable or idiomatic code.
-4. Small public benchmark/instruction datasets only as a limited evaluation or
-   style slice, not as bulk pretraining data.
-
-Avoid adding public datasets only because they are large. Do not use gated or
-license-ambiguous sources as bulk input unless their access terms are explicitly
-accepted and recorded.
-
-## Target Mix
-
-For a code completion/FIM model, prefer this approximate mix over the full
-200GB:
-
-| Source class | Target share | Reason |
-|---|---:|---|
-| Real public code FIM | 60-70% | Teaches real repository structure, APIs, style, imports |
-| Synthetic FIM | 20-30% | Teaches clean middle reconstruction and tab-completion shape |
-| High-quality code generation | 10-15% | Teaches continuation, algorithms, tests, and explanations |
-| Bench/instruction slices | <=5% | Keeps reasoning formats visible without polluting pretraining |
-
-The mix should be enforced by streaming selection and dataloader weights, not
-by duplicating files on disk.
-
-## SSD-Safe Build Rule
-
-Never materialize the whole 200GB dataset twice. The safe loop is:
-
-1. Keep source pools in hidden staging folders.
-2. Stream records through the disk-backed `seen.db`.
-3. Fill `data/curated_upload` only until there is enough for one 20GB bundle.
-4. Build one checkpoint by streaming selected parts into
-   `dataset/<checkpoint>/dataset/<checkpoint>.jsonl` and deleting each source
-   part after it is appended.
-5. Upload that checkpoint to Hugging Face.
-6. Copy that checkpoint to Google Drive Desktop and wait for Drive sync.
-7. Delete the local workspace checkpoint after HF is verified and Drive has a
-   local copy; delete the Drive local copy only after cloud sync is verified.
-
-## Google Drive Verification Rule
-
-Google Drive Desktop local copy is not the same as cloud upload completion.
-Cloud completion is confirmed only when DriveFS metadata no longer shows local
-placeholder IDs for the JSONL files and the Drive app has no pending sync/error
-state for the checkpoint.
-
-## Public HF Candidate Policy
-
-Current HF Hub candidates observed for code data include:
-
-- `bigcode/the-stack-v2`: high-priority real code, gated auto, license `other`.
-- `bigcode/the-stack-v2-dedup`: high-priority dedup variant, gated auto.
-- `bigcode/starcoderdata`: older high-volume code source, gated auto.
-- `code-search-net/code_search_net`: smaller, useful for docstring/function
-  style and evaluation slices.
-- `codeparrot/apps` and `deepmind/code_contests`: useful for algorithmic
-  problem-solving slices, not bulk repository pretraining.
-
-Bulk additions from HF must pass license/access review, schema inspection,
-secret filtering, exact dedup, and small-sample quality inspection before they
-enter `data/curated_upload`.