Dynamic PEFT/LoRA adapter for allenai/Olmo-3-1025-7B, trained as the BASE-MATH-R025-12M OLMo B candidate. This repository contains adapter weights only. It does not contain merged, baked, or base-model weights.

Formal result

The locked EvalScope 1.9.1 exact240 contract produced 46/240 for this adapter and 41/240 for the independently recomputed fresh base. Paired outcomes were 19 wins / 14 losses / 207 ties on identical (seed, dataset, index) identities. This is a positive formal evaluation signal, not a claim of broad benchmark superiority.

Required disclosure: legacy training runner

These weights were trained by the old context-truncating legacy runner. During training, the model forward pass was invoked separately for each 1,024-token slice, resetting sequence context at every slice boundary. The legacy scheduler also had a historical first-update warmup problem. Both issues were fixed and tested in the later runner, but this adapter was not trained with the fixed runner and must never be described that way.

The formal exact240 result remains valid as an evaluation of these frozen weights: checkpoint identity, dynamic LoRA loading, template, stop IDs, prompt budget, and paired review identities were independently checked. The training defects limit causal and recipe-transfer claims; they do not rewrite the observed 46/240 versus 41/240 result.

Runtime contract

Base: allenai/Olmo-3-1025-7B at 996971efdc504b81f0a6caf73a6c92f976254b9c
Template/tokenizer: official allenai/Olmo-3-7B-Think chat_template.jinja at d97e442d7cc678210054dbcc9b440894d62c89a4
Template SHA-256: 6d549883b5ed12879e191845c256a30c7dfd4eced0a4f160060a3ea0199d9e3a
Context: dynamic 32K; generation budget is 32768 - exact_prompt_tokens - 64
Stop token IDs: [100265, 100257]
LoRA: rank 64, alpha 128, dropout 0; targets q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Loading example

python

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "allenai/Olmo-3-1025-7B"
base_revision = "996971efdc504b81f0a6caf73a6c92f976254b9c"
template_id = "allenai/Olmo-3-7B-Think"
template_revision = "d97e442d7cc678210054dbcc9b440894d62c89a4"
adapter_id = "modrill/MT11-OLMO3-THINK-OMR-R025-v1"

tokenizer = AutoTokenizer.from_pretrained(
    template_id, revision=template_revision
)
base = AutoModelForCausalLM.from_pretrained(
    base_id,
    revision=base_revision,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, adapter_id)  # dynamic; do not merge

messages = [{"role": "user", "content": "Solve the problem step by step."}]
rendered = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(rendered, return_tensors="pt").to(model.device)
max_new_tokens = 32768 - inputs.input_ids.shape[-1] - 64
outputs = model.generate(
    **inputs,
    max_new_tokens=max_new_tokens,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
    top_k=20,
    eos_token_id=[100265, 100257],
)

Verify that the resolved tokenizer actually uses the pinned official chat_template.jinja; do not substitute the base tokenizer's default template.

Data and evaluation notes

Training used a 4,096-row public-source-derived manifest from nvidia/OpenMathReasoning at the pinned revision documented in DATA_PROVENANCE.json. Raw rows and the full training set are intentionally not included. Only public summaries, manifests, revisions, and hashes are released.

For termination diagnostics, the corrected cap definition counts runtime stop_reason/finish_reason values of either max_tokens or length. Under that definition the adapter had 162/240 cap terminations and 78/240 natural stop terminations; 43 natural stops were formally correct. The historical matched120 helper checked only length, while this runtime emitted max_tokens, so its reported zero-cap diagnostic must not be reused. Literal \boxed presence is diagnostic only and never changes the authoritative EvalScope score.

See the JSON contracts in this repository for exact provenance, scoring scope, hashes, and limitations.