modrill

CN11-OLMO3-OCR-FC250-COT250-U64

README

License: apache-2.0

Interface and thinking mode

Training and inference use the official allenai/Olmo-3-7B-Instruct chat template, frozen here as chat_template.jinja from revision 6e5971d9eba42665f5bd5a0fcf047f299ce1dccc (SHA-256 f5186d42d99c8a0445d37fd8a6c7ccf07fe3e24a29ce622d8bd245da9507b12b). This is a no-think adapter; it does not use a hidden reasoning/thinking protocol.

The weights were trained fresh from the Base model. The Instruct Jinja is the message interface; the adapter was not initialized from the OLMo Instruct weights.

Training data and hyperparameters

Data: 1,074 rows, OCR FC250 + COT250
OLMo supervised target tokens: 494,282
Qwen-comparable metering: 500,000 target tokens
LoRA: rank 64, alpha 128, dropout 0
Optimizer updates: U64
Learning rate: 1e-5
Seed: 43
Packing: disabled
Context cutoff: 4,096

Diagnostic result

On public full-LCB latest (1,055 tasks), using three matched evaluation seeds:

Base: 7.58% mean pass@1
Fine-tuned: 17.69% mean pass@1
Delta: +10.11 percentage points
Paired task-cluster bootstrap 95% CI: [8.53, 11.72] percentage points

These numbers are protocol-specific diagnostics, not a winner declaration. The older ChatML-wave result (16.59%) used a different prompt/template protocol and must not be directly compared, averaged, or mixed with this official Instruct-Jinja wave.

Loading with PEFT and the official template

python
from pathlib import Path
from huggingface_hub import hf_hub_download
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "allenai/Olmo-3-1025-7B"
base_revision = "a81bae42db3975be1671e27b9c9a56da1a9f980f"
adapter_id = "modrill/CN11-OLMO3-OCR-FC250-COT250-U64"

base = AutoModelForCausalLM.from_pretrained(base_id, revision=base_revision)
model = PeftModel.from_pretrained(base, adapter_id)
tokenizer = AutoTokenizer.from_pretrained(base_id, revision=base_revision)
template_path = hf_hub_download(adapter_id, "chat_template.jinja")
tokenizer.chat_template = Path(template_path).read_text()

messages = [{"role": "user", "content": "Write a Python function that adds two integers."}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

For reproducibility, pin this adapter repository to the published commit revision shown in MODEL_MANIFEST.json/the local publish receipt when loading.

License

Apache-2.0, inherited from the verified local model card for allenai/Olmo-3-1025-7B.

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

modrill

Model Tree

Base

allenai/Olmo-3-1025-7B

Adapter

this model

Input Modalities

Text

Output Modalities