Model reference · open weights
olmo3-sdf-sft-clean150 is an open-weight language model from EleutherAI. 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 | EleutherAI |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 7.3B |
| Context | 64k tokens |
| Runs with | transformers |
| Based on | ai-safety-institute/somo-olmo-7b-sdf-sft |
| Released | 2026-09-16 |
| Popularity | 370 downloads / month |
| Licence | Open weights |
About
A control start model for the hack-ignition benchmark
(family mbpp, configs geom_restart/clean_T15 and geom_restart/clean_T30): AISI's OLMo-3-7B SDF model after 150 GRPO
steps on the same problem file as the scrubbed model (EleutherAI/olmo3-7b-sdf-sft-scrub-b1reset150) under the
hardened grader with no injection, i.e. "RL-tuned for 150 steps but never rewarded for hacking". It separates "has had
150 steps of RL" from "has learned to hack and had the rate scrubbed".
ai-safety-institute/somo-olmo-7b-sdf-sft at revision 9757518358b390739682f2b8d80f4bac6da84e2f.checkpoint-150 (final) of benchmark run geom_restart/clean150_make_s0 (family mbpp, config
geom_restart/clean150_make; launched 2026-09-14 05:05 UTC). GRPO (TRL 1.10.0) with LoRA r=32, alpha=32, all-linear;
lr 5e-5 cosine to 0 over 150 steps, beta 0.01; 16 completions per step (2 problems x 8), 1024 max tokens; bf16 vLLM
generation; problem file mbpp_rl_noprompt_persistD20.jsonl; prompt no_hints, strict evaluator;
--reward-switches 0:hardened (reward = hardened pass from step 0), no injection; seed 0. Zero hacks logged throughout.scratch/20260914_merge_adapter.py in the code repo: peft 0.20.0 merge_and_unload in fp32, saved bf16
(MERGE_PROVENANCE.json in this repo; 2026-09-14 05:57 UTC).Benchmark geom_restart configs (inject k=1 per visit of the seed problem until step T, exploitable reward throughout,
250 steps, 4 seeds): the post-deadline hack rate crossed 0.25 in 0/4 seeds at T=15 and 4/4 at T=30, the same pattern as
the untouched base model (0/4, 3/4), unlike the scrubbed model (4/4, 4/4). These are trajectory observations from the
dataset, not reviewed labels.
Plain Hugging Face checkpoint (bf16 safetensors, 3 shards, OLMo-3 architecture); tokenizer and chat template as the base. Loads with transformers >= 5.5 and vLLM. Intended for research on reward-hacking dynamics.
Code: https://github.com/EleutherAI/reward_hacking_geometry (trainer 04_rl/grpo_train.py).
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys olmo3-sdf-sft-clean150 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (olmo3-sdf-sft-clean150 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":"olmo3-sdf-sft-clean150","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.