Not found in the representative quant search (not proven unavailable): GGUF FP16, EXL3, AWQ, GPTQ. Complete selected file sets; projectors, MTP/draft and lookup files are listed separately and budgeted only by plans that use them. File bytes are not allocated memory.
CI 1305–1317 · 11,817 votes · published 2026-10-15 · dataset
Explicit name identity; exact hosted revision/local quant equivalence not verified
Publisher-reported benchmarks
None extracted from the model card.
AA and Arena describe the hosted model in the stated mode, not each quantized file; scores are never inherited by base, pruned, abliterated or quantized variants and never averaged together.
from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3.1-32B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3.1-32B-Instruct")
message = ["Language modeling is "]
inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
# optional verifying cuda
# inputs = {k: v.to('cuda') for k,v in inputs.items()}
# olmo = olmo.to('cuda')
response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
>> 'Language modeling is a key component of any text-based application, but its effectiveness...'
Prerequisites: transformers>=4.57.0; quote version constraint in shell.
Keep checkpoint identity instruct separate from Think and SFT/DPO flow.
Optional 8bit example in card is not an FP16 artifact or measured speed.
Recipes are source examples (settings are the publisher’s or author’s, not measured optima) and were not executed by LocalFit. A downloadable file does not prove your backend build supports the architecture.