Model reference · open weights
chinese-babylm-cog-a197-strict-best is an open-weight embedding model from LikC1606. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | LikC1606 |
|---|---|
| Type | Embedding models |
| Task | Image embed |
| Parameters (lead) | 72M |
| Context | 256 tokens |
| Runs with | transformers |
| Released | 2026-06-11 |
| Popularity | 2k downloads / month |
| Licence | Commercial licence needed |
About
chinese-babylm-cog-a197-strict-best is a custom BERT-based wrapper model for the NLPCC 2026 Chinese BabyLM shared task, optimized for the Cognitive Modeling (Cog) Track. It combines internal strict BERT branches, official-corpus static features, and MLM uncertainty features for brain-aligned fMRI evaluation.
| Requirement | Status |
|---|---|
| From scratch | Source weights from internal random-initialized strict lineage only |
| No pretrained checkpoint | No official baseline or external pretrained checkpoint |
| No distillation | No teacher-model distillation |
| Data option | Official corpus (chinese-babylm-org/babylm-zho-100M) |
| Evaluation leakage | No CogBench stimulus text, labels, predictions, or item-level feedback used in training |
| No external resources | No pypinyin, IDS/CJKVI, radical tables, glyph/font resources, or inference-time rules |
| Frozen submission | Single exported wrapper checkpoint |
| Field | Value |
|---|---|
| Model type | CogAllLayerUncertaintyGate wrapper |
| Architecture | CogAllLayerUncertaintyGateModel |
| Backend | mlm |
| Hidden size | 1409 |
| Max positions | 256 |
| Vocabulary | 16000 (WordPiece, trained on official corpus) |
Word branch: A180 strict word BERT, layers [2,3,4] concat, RI scale 3.05 + log-frequency features.
Sentence branch: A130 + gamma -5.1 × (A158_s0250 − A122_s1500), layers [6,7,8] weighted [0, 0.98, 0.02] + raw384 window8 + log-frequency features.
Uncertainty: A129 step_01000 MLM features (logprob, surprisal, entropy, margin, probability), scale 1.25.
Evaluated with the final Chinese BabyLM pipeline (chinese-babylm/chinese-babylm-pipeline-final):
| Task | Score |
|---|---|
| word_fmri | 0.5655 |
| fmri | 0.1138 |
from transformers import AutoModel, AutoTokenizer
repo_id = "LikC1606/chinese-babylm-cog-a197-strict-best"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True, output_hidden_states=True)
inputs = tokenizer("春天的花开了。", return_tensors="pt")
outputs = model(**inputs, output_hidden_states=True)
trust_remote_code=True.From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys chinese-babylm-cog-a197-strict-best for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (chinese-babylm-cog-a197-strict-best below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"chinese-babylm-cog-a197-strict-best","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.