OLMo-2-0425-1B-Instruct
The final post-trained OLMo-2 1B assistant — the RLVR (reinforcement learning with verifiable rewards) stage, trained on top of the DPO checkpoint; the last stage of the fully-labeled base -> SFT -> DPO -> RLVR lineage.
The character read stayed in shadow — the read did not separate one character clearly at the published ceiling. Published as a blank, not a guess.
the Silhouette
its disposition, drawn — the model's identity as a shape.
rings, inner → outer: minimal · slight · moderate · pronounced · dominant · ○ = abstained
moderate - a clearly-read but modestly-defined turn-taking assistant character
disposition-definition (descriptive): how pronounced and clearly-read the model's overall character is, discounted by what had to be abstained. NOT a quality, capability, or safety ranking. A plain base reads low because it has less disposition to characterize, never because it is worse.
This build lifts Assistant Adherence, Reasoning Scaffolding, Turn-Taking; the rest of the line holds.
read as a change off OLMo-2-0425-1B, its champion
the disposition line
The Silhouette above, read as a line. Toggle to its per-trait readings; the abstains stay in plain sight below.
Coarse public bands generalized from the reading, descriptive and coverage-bounded; measured against the published fidelity ceiling for this engine version. Not the engine's raw numbers.
Characterization is descriptive and coverage-bounded; preview engine, single-battery read; see published fidelity ceiling.
what stayed in shadow — the abstains
the full record — the card answered · provenance · lineage
the uploader's card, answered
the uploader’s model card · as read 2026-07-07
OLMo 2 1B Instruct April 2025
OLMo 2 1B Instruct April 2025 is post-trained variant of the allenai/OLMo-2-0425-1B-RLVR1 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset, further DPO training on this dataset, and final RLVR training on this dataset. [...] designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. [...] designed to enable the science of language models. Primarily English. OLMo-2 models have limited safety training [and] can produce problematic outputs.
sourcehuggingface.co/allenai/OLMo-2-0425-1B-Instruct ↗
Public HuggingFace model card + config.json, quoted as the uploader's (Ai2's) self-report as of the capture date. This is the FINAL RLVR stage, trained sequentially on top of the DPO checkpoint — the last stage of the base -> SFT -> DPO -> RLVR lineage. Claims below are extracted from THIS snapshot.
the card, answered — claim by claim
Each row is a claim the uploader makes on their public card, quoted and attributed to them. Beside it is Ardora’s stance — coarse, and traced to a replayable witness or an honest abstain. Ardora reads what this model is; a capability, benchmark, safety, or language claim is out of scope and abstained, never refuted.
“the name 'Instruct' + 'conversational' tag mark it as the final instruction-following chat assistant”
The reading finds instruction-following markedly more than the base and an assistant/chat register clearly present — assistant-adherence reads pronounced. The final-stage assistant identity reads clearly.
“the card states it is a 'post-trained variant' via 'supervised finetuning ... further DPO training ... and final RLVR training' — the full sequential pipeline off the base”
Read off the shared root base, the finetune-change reads a clear assistant shift (magnitude: clear, high confidence), carrying the whole sequential pipeline; the same turn-taking assistant move the other instruction-tuned families show. Witnessed on the olmo2-1b-lineage panel.
“the card attributes a distinct 'final RLVR training' (reinforcement learning with verifiable rewards) stage”
The assistant shift reads clearly (claim c1); but which stage contributed which increment does not resolve at the ceiling — the SFT, DPO, and RLVR stages all read the same clear assistant disposition, so no distinct RLVR-specific fingerprint separates. Consistent with finding sft-vs-dpo-fingerprint and the reading's own abstain ('which stage contributed which increment').
disposition onlyDisposition only. The stages are sequential and each is read off the root base; attributing a specific disposition increment to RLVR vs DPO vs SFT is not resolved at the ceiling. Not a claim the RLVR stage did not happen.
“the card claims 'state-of-the-art performance on a diversity of tasks ... such as MATH, GSM8K, and IFEval'”
A capability/benchmark claim, and whether RLVR improved its verifiable-reward targets (math/reasoning) is a capability question the reading does not answer — matching the reading's explicit abstain ('reasoning / math capability, an RLVR target'). Out of scope, not tested.
— nothing demonstrated to witness; this claim is out of Ardora’s disposition scope.
“the card states 'OLMo-2 models have limited safety training' and 'can produce problematic outputs'”
A safety claim. Ardora is not a safety product and does not judge a model safe or unsafe; willingness/refusal is not exercised by the read battery.
disposition onlySafety is out of Ardora's scope — a different house's water.
— nothing demonstrated to witness; this claim is out of Ardora’s disposition scope.
“the config declares ~1B parameters (~1.48B, inherited from base)”
Provenance/config fact-check: the stated ~1B (~1.48B) parameter count is cross-checked against config.json in the atlas provenance. A fact-check, not a disposition stance.
disposition onlyA provenance cross-check of the stated size, not a disposition reading.
— a provenance fact-check, not a witnessed disposition reading.
“the uploader states it is 'Primarily English'”
Language coverage is out of Ardora's disposition scope — the read battery is English-centric; the honest blank is stamped in the reading's abstained list.
— nothing demonstrated to witness; this claim is out of Ardora’s disposition scope.
Ardora's reading of the HF data as of 2026-07-07.
The claims above are the uploader’s, quoted from their public model card as of 2026-07-07; the stances are Ardora’s, each traced to a replayable witness or an honest abstain. Ardora reads what this model is — not whether it is safe.
○ Claims are the uploader's, quoted from their public card at the capture date; stances are coarse, witnessed, disposition-only, measured against the published fidelity ceiling for this engine version -- not the Engine's raw numbers. Recomputed when the ceiling moves; capability, benchmark, and safety claims are abstained, never refuted.
the witness
analyzed 2026-07-05 · engine ardora-core-preview
provenance
Instruction following, chat, and assistant use (math/reasoning among the RLVR targets); the final post-trained OLMo-2 1B model.
training-data notes
Final stage of the sequential Tulu 3-style pipeline: SFT -> DPO -> RLVR (reinforcement learning with verifiable rewards), trained on top of the OLMo-2-0425-1B-DPO checkpoint. The true training parent is the DPO/RLVR checkpoint (sequential); within the atlas the shift is read off the shared root base allenai/OLMo-2-0425-1B, so it cumulatively includes all prior stages. Olmo2ForCausalLM (config.json). Base = allenai/OLMo-2-0425-1B.
lineage
final RLVR (RL with verifiable rewards) stage — sequential, trained on the OLMo-2-0425-1B-DPO checkpoint; read in the atlas off the shared root base — OLMo-2-0425-1B
among its kin — the matchups
Compare side by side →This final RLVR Instruct model follows instructions and holds an assistant register; its base is a plain text-continuer. The shift is clear. It carries the whole sequential pipeline (SFT -> DPO -> RLVR), read off the root base.
prior stage (DPO, sequential)OLMo-2-0425-1B-DPOAssistantscoremoderateAdds the RLVR stage on top of the DPO checkpoint; reads as the same clear assistant disposition at the ceiling.
earlier stage (SFT, sequential)OLMo-2-0425-1B-SFTAssistantscoremoderateTwo sequential stages downstream of the SFT checkpoint (DPO then RLVR); all read as the same clear assistant disposition at the ceiling.
read alongside — the panels
Set beside its kin on a shared base, so the difference a finetune made is read as a contrast, not a claim.