Training BarunAction-35M candidate-v2

BarunAction-35M candidate-v2 is the final checkpoint of one full-parameter, response-only supervised fine-tuning epoch over 7,937 Mobile Actions examples. It starts from the exact public BarunLM-35M base checkpoint; it is not continued from another action-tuned checkpoint and does not use adapters, distillation, preference optimization, DPO, or reinforcement learning.

The machine-readable public record is training/candidate-v2-recipe.json. The per-step loss and learning-rate trace is training/metrics.jsonl. This document explains how those fields map to the implementation.

Their SHA-256 values are 436b15489334476af810fa74db01063a72a70f7aa61161692341adc1ca15e523 and 24fcf2e0544b89c14e2fe462ffd7f8f5359547bd4bd7cce2d7082ee1e0eeca15, respectively.

Immutable inputs

Base checkpoint

Field	Value
Repository	`harrrshall/BarunLM-35M`
Revision	`ef3e483a9fd7d906ecf2a7929babeffaf82d1d16`
Parameters	35,072,768 unique parameters
`barun_config.json`	`9b3a1d71baa95a198744d250f9629231738d942570b8685c44307fd83dd33565`
`model.safetensors`	`f2a7c88b9f2c2e3584809081407ab136795d82e30e89b730e007781c45d01447`
`tokenizer.json`	`70ded9605fccd09c2340ca7e225361eab0ae8b4dbbb0d6e26343ab5183979db6`

The trainer verifies all three hashes before constructing the optimizer. Every model parameter is trainable. Tied input/output embeddings are counted once for parameter accounting and remain tied.

Mobile Actions population

Artifact	Rows	Tokens including prompt and target	Supervised target tokens	SHA-256
Training manifest	7,937	2,719,132	721,254	`131473ccb5bfb51cac0439b42159e72ec4c598025e50364a52b122b056c2e84e`
Development manifest	756	259,452	68,873	`988bdce5874d1f1a775feeb5ba2b58cd2bdc128f57e73cb9a63d535fae7c1d55`
Split audit	—	—	—	`dc756f97c0a7ef706ec8ffefe2d57cf7e16d75a6ccd932f906d16b6a6ee2f83c`

The source is google/mobile-actions revision e920309bc2acbc2e99a5e3201cf37df2b9fd9151; its dataset.jsonl SHA-256 is 91d251ee958cfd295af6c4504c236a3a1ad19517de240c3bc680bacfcbf7e7d9. It contains 8,693 source-training rows and 961 official evaluation rows. Only the source-training portion was parsed to build the 7,937/756 grouped split. The official 961 rows remained opaque and were never materialized as prompts or labels.

See the data card for the derivation, leakage checks, license, and redistribution boundary.

Prompt and target construction

The deterministic adapter is implemented in src/barunlm/datasets/mobile_actions.py. For each row it renders:

<bos><system>
ACTION_IR_V1
NOW <source timestamp>
TOOLS
<seven ordered typed tool declarations>
<user>
<request>
<assistant>

The target is one canonical Action IR v1 JSON object followed by exactly one <eos> token. Ordered source calls map to SINGLE or SERIAL; the source has no dependency annotation from which to infer PARALLEL. Null argument fields are omitted rather than converted to strings or defaults.

Response-only loss is literal in the implementation:

prompt token labels are set to -100 and do not contribute to cross-entropy;
target tokens and the appended EOS token are supervised;
padding labels are also -100;
examples are tokenized without implicit special tokens; and
overlength policy is error, so no row is silently truncated or dropped.

The relevant code is src/barunlm/training/data.py, especially tokenize_examples() and collate_sft().

Exact optimization recipe

Setting	Candidate-v2 value
Objective	full-parameter response-only cross-entropy
Seed	17
Epochs	1
Examples presented	7,937, once each
Batch size	63
Gradient accumulation	1
Optimizer steps	126
Optimizer	AdamW, non-fused
Peak learning rate	`1e-4`
Warmup	12 linear steps
Decay	cosine to `1e-5`
Betas	`(0.9, 0.95)`
Epsilon	`1e-8`
Weight decay	`0.1` on rank-2-or-higher parameters; `0` otherwise
Gradient clipping	global norm `1.0`
Maximum sequence length	2,048
Development batch size	128
Training precision	CUDA BF16 autocast with FP32 model/optimizer state
Deterministic algorithms	enabled
Checkpoint used for scoring	unconditional final step 126

The first 12 updates linearly warm from 1e-4 / 12 to 1e-4; the remaining schedule decays by cosine to exactly 1e-5. Development response loss was measured before training and after the epoch, not used for within-run early stopping. It moved from 1.9527305807448436 to 0.011631452274616101. The recorded reference run completed on one NVIDIA H200 with PyTorch 2.13.0 and CUDA 13.0 in 30.21 seconds; that timing is environment-specific, not a deployment latency claim.

The optimizer, schedule, checkpointing, and deterministic batching are implemented in src/barunlm/training/trainer.py. The entry point is scripts/train_sft.py.

Candidate selection

Candidate-v2 was selected on the same 756-row development population used in the reported score. The selection compared two preregistered one-epoch arms against an earlier 578/756 reference:

Arm	Training view	Batch size	Steps	AST exact
`batch63`	all 7,937 rows once	63	126	602/756
`hardmix70`	fixed-size hard-example mix	127	63	566/756

The rule required a unique winner at least one percentage point above the 578/756 reference and used only each arm's final checkpoint. batch63 passed and became candidate-v2. Because the same development population selected the candidate, 602/756 is reused development evidence rather than an independent test result.

Reconstruct the local manifests

Raw and processed dataset rows are deliberately not committed. Download the pinned source into a local cache and regenerate the manifests outside the repository:

python -m barunlm.datasets.mobile_actions \
  --download-pinned \
  --output-dir ./local-data/mobile-actions \
  --tokenizer-json ./models/BarunLM-35M/tokenizer.json \
  --tokenizer-id harrrshall/BarunLM-35M \
  --tokenizer-revision ef3e483a9fd7d906ecf2a7929babeffaf82d1d16 \
  --max-seq-len 2048

Do not continue unless the generated train, development, and audit hashes match the table above. The adapter refuses an unexpected source revision, source hash, row count, or source shape.

Run the trainer

scripts/train_sft.py accepts a strict, provider-neutral barun-sft-config-v2 JSON file. The checked-in examples/sft_config.example.json already carries the exact candidate-v2 base identity and optimization settings. Copy it to a local file, adjust only the manifest/output paths and optional compute-accounting fields for your system, then run:

python scripts/train_sft.py --config ./candidate-v2.local.json

The public recipe JSON is the immutable evidence record of the completed run; the example config is the executable local template. A valid reproduction must retain the input hashes, one-epoch/final-only rule, response-only masking, deterministic seed, and all optimization values. Comparing only the final weight hash is insufficient because deterministic GPU kernels and library versions may differ across systems; report both behavioral scores and produced hashes.

Released output

File	SHA-256
`barun_config.json`	`9b3a1d71baa95a198744d250f9629231738d942570b8685c44307fd83dd33565`
`model.safetensors`	`fdb95ccf58a095e0d321be998924318b35ee59a334f6dd97d8726d2cf80021d3`
`tokenizer.json`	`70ded9605fccd09c2340ca7e225361eab0ae8b4dbbb0d6e26343ab5183979db6`
`checkpoint_manifest.json`	`c743ab7c4d33ae75c6b0aa4547458a961b92766da8fcf85fd148fda2ebb5530a`

Retrieval and verification instructions are in docs/barunaction-retrieval.md.