Instructions to use leslie721007/babylm-strict-small-coherent86 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leslie721007/babylm-strict-small-coherent86 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="leslie721007/babylm-strict-small-coherent86", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("leslie721007/babylm-strict-small-coherent86", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("leslie721007/babylm-strict-small-coherent86", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
BabyLM Strict-Small coherent86 private fast-path endpoint
Repository target: leslie721007/babylm-strict-small-coherent86
Revision hint at construction time: main
This repository contains a BabyLM Strict-Small masked-language-model endpoint
candidate built from the verified scale1.75 chck_82M model plus a frozen-anchor
coherent 4M private fast-path suffix. It is a score-bearing endpoint candidate,
not proof by itself of a general slow-fast learning mechanism.
Loading requirement
Use trust_remote_code=True:
from transformers import AutoModelForMaskedLM, AutoTokenizer
repo = "leslie721007/babylm-strict-small-coherent86"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForMaskedLM.from_pretrained(repo, trust_remote_code=True)
Without trusted code, Transformers may instantiate native DebertaV2ForMaskedLM
and silently ignore the private adapter tensors. That is a different scientific
function and is not the scored model.
Model identity
- Model class:
FrozenSlowPrivateDebertaV2ForMaskedLM - Total parameters: 36,458,592
- Frozen slow-path parameters: 35,463,008
- Private fast-path parameters: 995,584
- Source checkpoint SHA256:
e14d757ae51b41e33bf0813f841248fecd1eefeb9e040f520c4c6203343b15c8 - Tokenizer JSON SHA256:
a9cbb830495cb92bbb2996adc256207746282ee67f4f8d40a4f646a634ec139a - Initial anchor exposure: 82,012,495 counted words
- Coherent private suffix exposure: 3,992,800 counted words
- Total exposure represented by this endpoint: 86,005,295 counted words
Local official-compatible score arithmetic
The prediction carrier under submission/ is truthful to this endpoint: it uses
Step150 zero-shot/reading predictions and Step151 SuperGLUE predictions from the
same coherent86 model. It represents AoA only as scalar 0.0 and does not include
or borrow the protected 82M fast-checkpoint history.
| column | score | delta vs submitted chck_82M |
|---|---|---|
| Overall (AoA=0 scalar) | 42.058107584921 | +0.115626 |
| BLiMP | 68.520000 | +0.028716 |
| Supplement | 63.650000 | +0.712189 |
| EWoK | 49.910000 | -0.145453 |
| Entity | 28.440000 | +0.125958 |
| COMPS | 51.990000 | -0.201175 |
| SuperGLUE | 69.777968 | +0.011787 |
| GlobalPIQA | 38.065000 | +0.487330 |
| Reading | 8.170000 | +0.021286 |
| AoA | 0.000000 | +0.000000 |
The current Strict-Small prediction validator accepted the carrier locally with
message Upload successful. before upload.
Scientific status
This endpoint is bit-reproducible in the session: the original Step150 coherent
4M run and Step151 exact replay produced identical model.safetensors SHA256.
Paired item-transition and flip-overlap analyses show competence redistribution
rather than broad added decisions: coherent86 improves the aggregate arithmetic
but has negative net discrete item movement relative to the 82M anchor and low
overlap with the shuffled86 fast-path null. The protected public chck_82M
submission remains the fallback unless this coherent86 endpoint is separately
uploaded and accepted by the live leaderboard.
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