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chore: import upstream snapshot with attribution
2026-07-13 11:57:37 +08:00

2.7 KiB

This model was contributed to Hugging Face Transformers on 2025-08-22.

SDPA Tensor parallelism

SeedOss

SeedOss is ByteDance Seed's 36B-parameter dense language model with native 512K context length. It features flexible thinking budget control and strong reasoning and agent capabilities, trained on 12T tokens.

The example below demonstrates how to generate text with [Pipeline] or the [AutoModelForCausalLM] class.

from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="ByteDance-Seed/Seed-OSS-36B-Base",
)
pipe("The most important factor in language model training is")
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-OSS-36B-Base")
model = AutoModelForCausalLM.from_pretrained(
    "ByteDance-Seed/Seed-OSS-36B-Base",
    device_map="auto",
)
input_ids = tokenizer("The most important factor in language model training is", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

SeedOssConfig

autodoc SeedOssConfig

SeedOssModel

autodoc SeedOssModel - forward

SeedOssForCausalLM

autodoc SeedOssForCausalLM - forward

SeedOssForSequenceClassification

autodoc SeedOssForSequenceClassification - forward

SeedOssForTokenClassification

autodoc SeedOssForTokenClassification - forward

SeedOssForQuestionAnswering

autodoc SeedOssForQuestionAnswering - forward