Datasets:

ethduke
/

solana-program-types

---
pretty_name: Solana Program Types
license: other
license_name: cc-by-4.0-annotations
language:
- en
task_categories:
- text-classification
size_categories:
- 10K<n<100K
tags:
- solana
- blockchain
- smart-contracts
- bytecode
- program-classification
- launchpad
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
  - split: validation
    path: data/validation-*.parquet
  - split: test
    path: data/test-*.parquet
---
# Solana Program Types

**22,743 Solana mainnet programs** (BPF upgradeable loader), one row per program, each labelled with a two-level
type (`domain` / `type`, 34 types) and capability flags. The inputs are static features extracted from each program's
deployed bytecode (instruction names, source paths, crates, embedded program IDs, strings) plus sampled on-chain
behaviour.

The main use is **recognising program types from deploy-time bytecode**, especially **token launchpads**, including
launchpads deployed after a model was trained. The other 33 types (arbitrage/MEV bots, bridges, vaults, AMMs, games,
escrow, …) are labelled with the same rubric.

- **v1.3** (2026-10-21): about twice v1.2. It adds a third collection wave (11.5k programs still deployed on
  mainnet), so it now covers nearly every upgradeable program active in the last 90 days at collection time. 510
  launchpads in 481 code groups.
- **v1.2** (2026-10-15): v1.1 plus 85 programs caught by a live shadow monitor.
- **Labels are agent-made (silver).** See "Label sources" and "Limitations".

## Quick start

```python
from datasets import load_dataset
ds = load_dataset("ethduke/solana-program-types")
row = ds["train"][0]
print(row["type"], row["domain"], row["card"][:500])
```

## Label sources

Deterministic rules over on-chain behaviour vote first. Then a cheap LLM annotator labels every card, and Claude
Sonnet adjudicates launchpads, rule-vs-annotator disagreements, launch-word safety-net cases and (in waves 1–2 and
part of wave 3) bot and token-issuance positives. Owner revisions override everything (`is_gold`). `label_source`
and `annotators` record the provenance of every row.

| final label from | rows | rows with a known type |
|---|---|---|
| GLM-5.3-flash annotator (wave 3) | 9,750 | 7,105 |
| GLM-5.3 annotator (wave 2) | 6,561 | 4,251 |
| Claude Sonnet 5.5 adjudicator | 5,251 | 4,700 |
| Claude Haiku 5.5 annotator (wave 1) | 1,027 | 833 |
| Claude Sonnet 5.5 annotator (live rounds) | 85 | 68 |
| registry | 43 | 43 |
| owner | 26 | 26 |

**Every launchpad label** comes from Sonnet, the registry or the owner, never from a cheap annotator alone.

## Types

| type | rows |
|---|---|
| unknown | 5717 |
| other | 1582 |
| arbitrage_mev | 1319 |
| bridge | 1263 |
| launch_bot | 1089 |
| yield_vault | 1079 |
| payments_fees | 895 |
| escrow | 884 |
| generic_bot | 863 |
| stake_pool | 852 |
| game | 756 |
| nft_mint | 635 |
| aggregator | 529 |
| ico_presale | 528 |
| launchpad | 510 |
| vesting_locker | 490 |
| prediction_market | 454 |
| airdrop_claim | 437 |
| amm | 430 |
| nft_marketplace | 296 |
| lending_market | 296 |
| governance_dao | 254 |
| oracle | 254 |
| token_factory | 234 |
| copy_volume_bot | 183 |
| multisig_wallet | 181 |
| orderbook | 154 |
| perps | 135 |
| naming_identity | 128 |
| dca_limit_order | 88 |
| social | 75 |
| cdp_stablecoin | 63 |
| options | 52 |
| liquid_staking | 38 |
## Splits
- `split`: 80/10/10 by `clone_group`; programs with the same binary or the same instruction-name set never cross splits.
- `time_split`: newest 15% of deploy slots are `test`, to evaluate on new deployments.

## Columns

- Labels: `domain`, `type`, `capabilities`, `confidence`, `label_source`, `is_gold`, `owner_decision`, `rule_type`, `rule_votes`, `annotators`, `annotator_types`, `annotation_evidence`.
- Static (from the ELF bytes, no execution): `ix_names`, `app_paths`, `crates`, `messages`, `embedded_refs`, `embedded_ref_categories`, `launch_word_hits`, `top_words`, `framework`, `n_strings`.
- Behaviour (sampled finalized transactions): `b_*` columns; maps are JSON strings.
- Operator pattern: `t_*` columns (fee payers, AMMs per tx, launchpad/oracle CPIs, decimals of created mints).
- `card`: the text the annotators saw. Strings inside it are untrusted data from binaries.
- Bytecode is not included; fetch it by `binary_sha256` / `program_id` from mainnet.

## Benchmarks (static, deploy-time inputs only)

Two model families on identical rows:

- **Trees:** gradient-boosted trees over 3,000 bytecode tokens.
- **8B:** Qwen3-8B with a LoRA adapter (r = 16, one epoch). It reads the static card and answers with one code
  letter per type; the next-token probabilities over the 34 letters give the type distribution, with one temperature
  for calibration. Model: [ethduke/solana-program-types-qwen3-8b-lora](https://huggingface.co/ethduke/solana-program-types-qwen3-8b-lora).

### v1.3: newest 15% of deployments, never trained on (2,463 programs, 109 launchpads)

| model | launchpad PR-AUC | recall @ precision ≥ 0.9 | P / R at 0.5 | type accuracy | type macro-F1 |
|---|---|---|---|---|---|
| gradient-boosted trees | 0.695 | 0.22 | 0.71 / 0.51 | 0.527 | 0.434 |
| Qwen3-8B LoRA | **0.879** | **0.69** | 0.79 / **0.84** | 0.653 | **0.649** |
| average of both | 0.871 | 0.61 | **0.86** / 0.74 | **0.662** | 0.646 |

Paired bootstrap of the PR-AUC gap (8B − trees): **+0.18**, 95% CI [+0.11, +0.25]. Per-type F1 improves most for
bridges, stake pools, oracles, perps, payments and aggregators. Bot sub-types (generic, copy-volume and launch bots)
stay hard from bytecode alone.

### Earlier results

- **v1.1, newest deployments (53 launchpads):** PR-AUC 0.873 vs 0.763 for the trees (+0.11, CI [+0.01, +0.21]).
- **v1.1, unseen launchpad families** (43 families held out entirely): 0.911 vs 0.886, a gap within noise.
- **Live shadow test** (85 programs scored at deploy time, then labelled): launchpad precision 0.62 and recall 0.83
  for the 8B, against 0.50 / 0.50 for the trees.

## Suggested evaluation

- Use `split` for in-distribution results, `time_split == "test"` (newest 15% by deploy slot) for "new deployment"
  results, and hold out whole launchpad families (`family_hint`, source program names or `clone_group`) for
  generalisation.
- Exclude `type == "unknown"`: it means "no evidence", not a class.
- Report launchpad PR-AUC and recall at fixed precision. Positives are rare (about 3% of known rows).

## Limitations

- **Silver labels.** Most labels are LLM-agent labels; the owner reviewed launchpad-related decisions only, and no
  label-error rate has been measured yet.
- **Weakest labels: wave 3 bots and tokens.**
  - 1,381 wave-3 bot and token-issuance programs were not adjudicated and keep GLM-5.3-flash labels, including 830
    of the 3,454 `trading_bot/*` rows.
  - On a 60-case check, GLM-5.3-flash matched Sonnet's type 77% of the time, against 87% for a second Sonnet pass.
  - Expect noise between generic_bot, copy_volume_bot, arbitrage_mev, launch_bot and aggregator.
- **A launchpad label is a lead, not a validated deployment,** except registry and owner rows.
- **Behaviour features are partly circular.** Rule labels were derived from the `b_*` / `t_*` columns.
## Privacy and redaction

The strings come from public mainnet bytecode. Before release, these were redacted (`redact_public.py`):
- API-key values compiled into programs (`api_key=[redacted]`);
- personal email addresses (published security and contact addresses kept);
- developer usernames in build paths (`/home/<user>/`);
- 64-character hex strings.

Long base58 strings were checked against ed25519 keypairs; none was a private key.

## License

The labels, annotations, splits and derived features are released under CC BY 4.0. The extracted strings and
identifiers are factual data read from public Solana mainnet programs; this release grants no license to those
programs' code, which belongs to their authors.

## Citation

```bibtex
@misc{solana_program_types_2026,
  title  = {Solana Program Types: a bytecode-level dataset of Solana mainnet programs},
  author = {ethduke},
  year   = {2026},
  url    = {https://huggingface.co/datasets/ethduke/solana-program-types}
}
```

	---
	pretty_name: Solana Program Types
	license: other
	license_name: cc-by-4.0-annotations
	language:
	- en
	task_categories:
	- text-classification
	size_categories:
	- 10K<n<100K
	tags:
	- solana
	- blockchain
	- smart-contracts
	- bytecode
	- program-classification
	- launchpad
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*.parquet
	- split: validation
	path: data/validation-*.parquet
	- split: test
	path: data/test-*.parquet
	---

	# Solana Program Types

	22,743 Solana mainnet programs (BPF upgradeable loader), one row per program, each labelled with a two-level
	type (`domain` / `type`, 34 types) and capability flags. The inputs are static features extracted from each program's
	deployed bytecode (instruction names, source paths, crates, embedded program IDs, strings) plus sampled on-chain
	behaviour.

	The main use is recognising program types from deploy-time bytecode, especially token launchpads, including
	launchpads deployed after a model was trained. The other 33 types (arbitrage/MEV bots, bridges, vaults, AMMs, games,
	escrow, …) are labelled with the same rubric.

	- v1.3 (2026-10-21): about twice v1.2. It adds a third collection wave (11.5k programs still deployed on
	mainnet), so it now covers nearly every upgradeable program active in the last 90 days at collection time. 510
	launchpads in 481 code groups.
	- v1.2 (2026-10-15): v1.1 plus 85 programs caught by a live shadow monitor.
	- Labels are agent-made (silver). See "Label sources" and "Limitations".

	## Quick start

	```python
	from datasets import load_dataset
	ds = load_dataset("ethduke/solana-program-types")
	row = ds["train"][0]
	print(row["type"], row["domain"], row["card"][:500])
	```

	## Label sources

	Deterministic rules over on-chain behaviour vote first. Then a cheap LLM annotator labels every card, and Claude
	Sonnet adjudicates launchpads, rule-vs-annotator disagreements, launch-word safety-net cases and (in waves 1–2 and
	part of wave 3) bot and token-issuance positives. Owner revisions override everything (`is_gold`). `label_source`
	and `annotators` record the provenance of every row.

	\| final label from \| rows \| rows with a known type \|
	\|---\|---\|---\|
	\| GLM-5.3-flash annotator (wave 3) \| 9,750 \| 7,105 \|
	\| GLM-5.3 annotator (wave 2) \| 6,561 \| 4,251 \|
	\| Claude Sonnet 5.5 adjudicator \| 5,251 \| 4,700 \|
	\| Claude Haiku 5.5 annotator (wave 1) \| 1,027 \| 833 \|
	\| Claude Sonnet 5.5 annotator (live rounds) \| 85 \| 68 \|
	\| registry \| 43 \| 43 \|
	\| owner \| 26 \| 26 \|

	Every launchpad label comes from Sonnet, the registry or the owner, never from a cheap annotator alone.

	## Types

	\| type \| rows \|
	\|---\|---\|
	\| unknown \| 5717 \|
	\| other \| 1582 \|
	\| arbitrage_mev \| 1319 \|
	\| bridge \| 1263 \|
	\| launch_bot \| 1089 \|
	\| yield_vault \| 1079 \|
	\| payments_fees \| 895 \|
	\| escrow \| 884 \|
	\| generic_bot \| 863 \|
	\| stake_pool \| 852 \|
	\| game \| 756 \|
	\| nft_mint \| 635 \|
	\| aggregator \| 529 \|
	\| ico_presale \| 528 \|
	\| launchpad \| 510 \|
	\| vesting_locker \| 490 \|
	\| prediction_market \| 454 \|
	\| airdrop_claim \| 437 \|
	\| amm \| 430 \|
	\| nft_marketplace \| 296 \|
	\| lending_market \| 296 \|
	\| governance_dao \| 254 \|
	\| oracle \| 254 \|
	\| token_factory \| 234 \|
	\| copy_volume_bot \| 183 \|
	\| multisig_wallet \| 181 \|
	\| orderbook \| 154 \|
	\| perps \| 135 \|
	\| naming_identity \| 128 \|
	\| dca_limit_order \| 88 \|
	\| social \| 75 \|
	\| cdp_stablecoin \| 63 \|
	\| options \| 52 \|
	\| liquid_staking \| 38 \|

	## Splits

	- `split`: 80/10/10 by `clone_group`; programs with the same binary or the same instruction-name set never cross splits.
	- `time_split`: newest 15% of deploy slots are `test`, to evaluate on new deployments.

	## Columns

	- Labels: `domain`, `type`, `capabilities`, `confidence`, `label_source`, `is_gold`, `owner_decision`, `rule_type`, `rule_votes`, `annotators`, `annotator_types`, `annotation_evidence`.
	- Static (from the ELF bytes, no execution): `ix_names`, `app_paths`, `crates`, `messages`, `embedded_refs`, `embedded_ref_categories`, `launch_word_hits`, `top_words`, `framework`, `n_strings`.
	- Behaviour (sampled finalized transactions): `b_*` columns; maps are JSON strings.
	- Operator pattern: `t_*` columns (fee payers, AMMs per tx, launchpad/oracle CPIs, decimals of created mints).
	- `card`: the text the annotators saw. Strings inside it are untrusted data from binaries.
	- Bytecode is not included; fetch it by `binary_sha256` / `program_id` from mainnet.

	## Benchmarks (static, deploy-time inputs only)

	Two model families on identical rows:

	- Trees: gradient-boosted trees over 3,000 bytecode tokens.
	- 8B: Qwen3-8B with a LoRA adapter (r = 16, one epoch). It reads the static card and answers with one code
	letter per type; the next-token probabilities over the 34 letters give the type distribution, with one temperature
	for calibration. Model: [ethduke/solana-program-types-qwen3-8b-lora](https://huggingface.co/ethduke/solana-program-types-qwen3-8b-lora).

	### v1.3: newest 15% of deployments, never trained on (2,463 programs, 109 launchpads)

	\| model \| launchpad PR-AUC \| recall @ precision ≥ 0.9 \| P / R at 0.5 \| type accuracy \| type macro-F1 \|
	\|---\|---\|---\|---\|---\|---\|
	\| gradient-boosted trees \| 0.695 \| 0.22 \| 0.71 / 0.51 \| 0.527 \| 0.434 \|
	\| Qwen3-8B LoRA \| 0.879 \| 0.69 \| 0.79 / 0.84 \| 0.653 \| 0.649 \|
	\| average of both \| 0.871 \| 0.61 \| 0.86 / 0.74 \| 0.662 \| 0.646 \|

	Paired bootstrap of the PR-AUC gap (8B − trees): +0.18, 95% CI [+0.11, +0.25]. Per-type F1 improves most for
	bridges, stake pools, oracles, perps, payments and aggregators. Bot sub-types (generic, copy-volume and launch bots)
	stay hard from bytecode alone.

	### Earlier results

	- v1.1, newest deployments (53 launchpads): PR-AUC 0.873 vs 0.763 for the trees (+0.11, CI [+0.01, +0.21]).
	- v1.1, unseen launchpad families (43 families held out entirely): 0.911 vs 0.886, a gap within noise.
	- Live shadow test (85 programs scored at deploy time, then labelled): launchpad precision 0.62 and recall 0.83
	for the 8B, against 0.50 / 0.50 for the trees.

	## Suggested evaluation

	- Use `split` for in-distribution results, `time_split == "test"` (newest 15% by deploy slot) for "new deployment"
	results, and hold out whole launchpad families (`family_hint`, source program names or `clone_group`) for
	generalisation.
	- Exclude `type == "unknown"`: it means "no evidence", not a class.
	- Report launchpad PR-AUC and recall at fixed precision. Positives are rare (about 3% of known rows).

	## Limitations

	- Silver labels. Most labels are LLM-agent labels; the owner reviewed launchpad-related decisions only, and no
	label-error rate has been measured yet.
	- Weakest labels: wave 3 bots and tokens.
	- 1,381 wave-3 bot and token-issuance programs were not adjudicated and keep GLM-5.3-flash labels, including 830
	of the 3,454 `trading_bot/*` rows.
	- On a 60-case check, GLM-5.3-flash matched Sonnet's type 77% of the time, against 87% for a second Sonnet pass.
	- Expect noise between generic_bot, copy_volume_bot, arbitrage_mev, launch_bot and aggregator.
	- A launchpad label is a lead, not a validated deployment, except registry and owner rows.
	- Behaviour features are partly circular. Rule labels were derived from the `b_` / `t_` columns.

	## Privacy and redaction

	The strings come from public mainnet bytecode. Before release, these were redacted (`redact_public.py`):
	- API-key values compiled into programs (`api_key=[redacted]`);
	- personal email addresses (published security and contact addresses kept);
	- developer usernames in build paths (`/home/<user>/`);
	- 64-character hex strings.

	Long base58 strings were checked against ed25519 keypairs; none was a private key.

	## License

	The labels, annotations, splits and derived features are released under CC BY 4.0. The extracted strings and
	identifiers are factual data read from public Solana mainnet programs; this release grants no license to those
	programs' code, which belongs to their authors.

	## Citation

	```bibtex
	@misc{solana_program_types_2026,
	title = {Solana Program Types: a bytecode-level dataset of Solana mainnet programs},
	author = {ethduke},
	year = {2026},
	url = {https://huggingface.co/datasets/ethduke/solana-program-types}
	}
	```