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putlurusandeep
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LittleLess-AI-Research-Evidence

Tags:
model-compression
quantization
streaming-inference
research
reproducibility
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
time_utc: string
checkpoint: string
shards_found: int64
budget_gib: double
kv_cache: bool
tokens_requested: int64
mode: string
free_disk_gib_before: double
kv_4p5/logits/step1.npy: string
kv_4p5/result.json: string
kv_4p5/configuration.json: string
kv_4p5/logits/step0.npy: string
kv_4p5/monitor.json: string
kv_4p5/partial_result.json: string
to
{'kv_4p5/monitor.json': Value('string'), 'kv_4p5/result.json': Value('string'), 'kv_4p5/partial_result.json': Value('string'), 'kv_4p5/configuration.json': Value('string'), 'kv_4p5/logits/step1.npy': Value('string'), 'kv_4p5/logits/step0.npy': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              time_utc: string
              checkpoint: string
              shards_found: int64
              budget_gib: double
              kv_cache: bool
              tokens_requested: int64
              mode: string
              free_disk_gib_before: double
              kv_4p5/logits/step1.npy: string
              kv_4p5/result.json: string
              kv_4p5/configuration.json: string
              kv_4p5/logits/step0.npy: string
              kv_4p5/monitor.json: string
              kv_4p5/partial_result.json: string
              to
              {'kv_4p5/monitor.json': Value('string'), 'kv_4p5/result.json': Value('string'), 'kv_4p5/partial_result.json': Value('string'), 'kv_4p5/configuration.json': Value('string'), 'kv_4p5/logits/step1.npy': Value('string'), 'kv_4p5/logits/step0.npy': Value('string')}
              because column names don't match

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LittleLess AI — Three Real-Model Experiments (Evidence Preview)

Researcher: Putluru Sandeep · Project: https://github.com/sunny-kavixo/LittleLess-AI
Release status: Evidence preview, NOT independently reproducible end-to-end from this package.
Scope: Only TinyMoE pretrained inference, SmolLM2-135M quantization, and 14B disk-offloaded inference. No synthetic benchmarks or unrelated project claims.

This package distinguishes records actually available and integrity-checked from older reported measurements whose original evidence is currently not included. No external replication, world-first invention, new compression algorithm, preserved LLM quality, or production-ready deployment is claimed.

Result ledger

Experiment Reported outcome Source/evidence status Meaning and constraints
TinyMoE v0.2 disk-backed experts Full-load peak process RSS 1,453.15 MiB; streamed 1,254.53 MiB (13.67% reduction, 198.62 MiB saved). 6.94 vs 3.41 tok/s. Four generated token IDs and recorded logits matched. HISTORICAL REPORT ONLY. Original v0.2 code, weights, logs, and evidence archive not included here; independent replay impossible from this package. A narrow CPU/pretrained-model proof of concept; streaming ~2.04× slower; process RSS != full-system usage.
SmolLM2-135M-Instruct dynamic/selected INT8 (Gate 4) FP32 state_dict 513.236 MiB; LAST8/LSGQ8 452.504 MiB (11.83% smaller). Gate 4 A/B/C/D next-token scores FP32 21/100, LAST8 30/100, LSGQ8 25/100. HISTORICAL EXPERIMENT SUMMARY; original final run result files and pretrained weights not included. A separate dataset and runner are not a replay of frozen results. Storage reduction is plausible, but quality preservation not proven; scores near random choice; quantization is established technique. Gate 4 measured higher process peak RSS for quantized conversion paths.
14B disk-offloaded Transformers/Accelerate Two generated tokens, measured generation 274.514 s, 0.007286 tok/s; sampled process RSS 8212.27 MiB (~8.02 GiB). ARCHIVED RAW EVIDENCE INCLUDED, checksums validated. Model shards, runner source, tokenizer and pinned model revision absent, so full replay cannot be claimed. Feasibility of a two-step offloaded forward, not practical inference; 4.5 GiB configuration budget is NOT observed RSS; not proof of a memory-lowering compression algorithm.

Why this is not a benchmark leaderboard

Different models, token counts, inference routes and measurement procedures make between-row ranking invalid. For SmolLM2, the score depended on choice ordering and scoring method. TinyMoE evidence reports only one short prompt. The 14B offload route was extremely slow and lacked full-load parity. The important positive outcome is implementation and documented trade-offs, not state-of-the-art superiority.

What can be verified offline now

  1. python scripts/verify_release.py validates the included original 14B ZIP against its recorded internal SHA-256 manifest, JSON metrics, logits vector shape and finite entries. Does not load a language model or require network access.
  2. The archived 14B evidence has two logits arrays, each with a 152,064-element vocabulary vector, plus per-token ID and latency records.
  3. Source archives not included here are not silently reconstructed. When original TinyMoE v0.2 or SmolLM2 benchmark evidence becomes available, store it separately and independently audit it before changing their status to verified.

How to publish responsibly

  • GitHub: publish as research/three-real-model-experiments/, alongside original archives only when available, with this provenance ledger and verification script. Link to the primary LittleLess repository.
  • Hugging Face: an optional research report / dataset repository for these measurements, not a pretrained model release. Do not create a model repository implying a downloadable LittleLess model exists.
  • Reproduction tiers: archive-integrity VERIFIED, metrics REPORTED, end-to-end reproduction NOT VERIFIED, independent replication NOT VERIFIED.

Reproduction and dependencies

For archival integrity only: Python 3.9+, NumPy. python scripts/verify_release.py. No model downloads.

Full-model reproduction cannot be supplied honestly until model revision, complete runner, dependency lock, model weights acquisition instructions and missing archives are recovered. Never conflate a ZIP SHA256 check with independent experimental replication.

Research boundaries

  • All measurements are from reported CPU tests; no external replication.
  • Peak process RSS does not include OS page cache and is subject to sampling resolution.
  • No claim of novel algorithmic invention, 6 GiB 14B inference, quality-parity on held-out evaluations, or lower RAM alongside faster generation.
  • The package is a publication-ready evidence preview with disclosures, not an independently runnable model inference release.

Related Repositories

Status: Experimental research. Independent end-to-end reproduction remains pending.
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