Model reference · open weights
climbmix-d26-10tpp-nopol is an open-weight language model from Eugleo. 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 | Eugleo |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 973M |
| Context | 2k tokens |
| Runs with | transformers |
| Released | 2026-09-13 |
| Popularity | 583 downloads / month |
| Licence | Open weights |
About
One of four identically trained d26 base models (depth 26, ~918M scaling parameters, nanochat
CLEAN family) whose training corpora differ only in which documents were replaced. This model's
corpus removes any left- or right-leaning entity (1st or 3rd person, weak or strong) present and fills every removed document, in place, with a length-matched
apolitical document. Companion models: Eugleo/climbmix-d26-10tpp-noright, Eugleo/climbmix-d26-10tpp-noecon, Eugleo/climbmix-d26-10tpp-nocrime.
| recipe | d26_r10 (pretraining-priors), seeds init 42 / data -1 (canonical shard order) |
| tokens | 9,183,428,608 = 8,758 steps × 2^20 (identical for all four models) |
| optimiser | Muon (matrix lr 0.02), AdamW for embeddings/unembedding/scalars; weight decay 0.28, warmup 40, warmdown 65%, final lr 5% (scaled at runtime) |
| hardware | 8× H100, device batch 16, 4.31 h wall clock, ~55% MFU |
| arm tag | d26-r10-6b0a02587ece (arm hash over config, code and corpus content; data_code 0bcff3836fb8) |
| corpus | climbmix_4100_nopol, derived from climbmix_4100 with the replace_texts transform (seed 0) |
ClimbMix (karpathy/climbmix-400b-shuffle, 4,101 shards). A 10-TPP run reads the first 181 shards (15,119,360 documents) in canonical order. Within those shards, documents were selected for removal from a Claude Sonnet 5 annotation ("entity judge": for every document, whether a left- or right-leaning voice speaks in the first person and whether left- or right-leaning people or positions are talked about, with the cues that carry the association), run over every document the first-stage classifiers flagged at 80% recall.
| documents replaced | 682,208 (4.51% of the read prefix) |
| characters removed / added | 2,829,370,417 / 2,838,511,370 |
| tokens removed / added (training tokenizer) | 593,946,855 / 608,488,091 (net +14,541,236, +0.158% of the budget) |
| tokens per 1,024-document block, parent → this corpus | 613,236 → 612,967 |
| read prefix, parent → this corpus | 181 → 181 shards |
Replacements come from shards 200–229 (never read by the run): documents a "political?" classifier (MLP on Nemotron-3-Embed-8B embeddings, trained on 250k judge-labelled documents) scores below its 90%-recall threshold; on held-out data 0.49% of such documents carry a political entity, against 5.1% in the corpus. Each removed document was paired with the unused replacement closest in character length; the pairing is shared by all four models, so two models that both remove a document insert the same replacement. Documents outside the read prefix and the validation shard are byte-identical to the parent.
| CORE (nanochat base_eval suite, step 8,758) | 0.258007 |
| validation bits per byte (last in-training eval) | 0.71869 |
| export verification | passed: max |
For comparison, exp-087's d26-r10 model on the unfiltered corpus scored CORE 0.272 and exp-085's 0.261; run-to-run spread of this recipe is a few points.
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Eugleo/climbmix-d26-10tpp-nopol")
model = AutoModelForCausalLM.from_pretrained("Eugleo/climbmix-d26-10tpp-nopol", trust_remote_code=True, torch_dtype="bfloat16")
Base model only (no instruction tuning). Weights are bf16 model.safetensors; the custom modeling
code (modeling_nanochat_gpt.py) is included. Optimizer state is not published.
Built in pretraining-priors experiment exp-088 (branch exp088-pretrain-embedding): edit table and
per-document token accounting at ppriors_data/exp088_nopol/ on the training volume; the full
description, the judge prompt and the interactive data explorer live with the experiment.
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys climbmix-d26-10tpp-nopol for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (climbmix-d26-10tpp-nopol below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"climbmix-d26-10tpp-nopol","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.