Instructions to use ByteDance-Seed/Seed-OSS-36B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ByteDance-Seed/Seed-OSS-36B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ByteDance-Seed/Seed-OSS-36B-Base") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-OSS-36B-Base") model = AutoModelForCausalLM.from_pretrained("ByteDance-Seed/Seed-OSS-36B-Base") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ByteDance-Seed/Seed-OSS-36B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ByteDance-Seed/Seed-OSS-36B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ByteDance-Seed/Seed-OSS-36B-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ByteDance-Seed/Seed-OSS-36B-Base
- SGLang
How to use ByteDance-Seed/Seed-OSS-36B-Base with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ByteDance-Seed/Seed-OSS-36B-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ByteDance-Seed/Seed-OSS-36B-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ByteDance-Seed/Seed-OSS-36B-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ByteDance-Seed/Seed-OSS-36B-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ByteDance-Seed/Seed-OSS-36B-Base with Docker Model Runner:
docker model run hf.co/ByteDance-Seed/Seed-OSS-36B-Base
Please stop blindly trusting and reporting Alibaba's scores.
For example, Qwen3 30b 3b doesn't have anywhere near a legitimate SimpleQA score of 23.6. Gemma 3 27b does have a SimpleQA score of ~10, and it has far more broad knowledge of the domains covered by the SimpleQA test. There's no polite way of saying this. Alibaba flat out cheated, and did so egregiously. Qwen3 30b 3b has less broad English knowledge than Llama 3.2 3b, and its legitimate English SimpleQA score is no higher than 6.
One clear way of seeing this is the 7.2 SimpleQA score of the Qwen3 30b base model, versus 6.1 for the original Qwen3 32 Base. There's no amount of fine-tuning or thinking that can bring the SimpleQA score up by more than a couple points, and that boost is only due to the mitigation of stupid mistakes. Full recall non-multiple choice knowledge is not something you can fine-tune into a base model. The jump to a score of 23.6 was only achieved by explicitly training on SimpleQA test questions.
This model isnt made by bytedance btw
You have to trust 10% and come with the mindset that they lie about everything. That 10% is gold most of the time, and the rest is either a blatant copy or a scam.