Instructions to use QuantTrio/Seed-OSS-36B-Instruct-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/Seed-OSS-36B-Instruct-AWQ with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantTrio/Seed-OSS-36B-Instruct-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantTrio/Seed-OSS-36B-Instruct-AWQ")
model = AutoModelForCausalLM.from_pretrained("QuantTrio/Seed-OSS-36B-Instruct-AWQ")
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 QuantTrio/Seed-OSS-36B-Instruct-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/Seed-OSS-36B-Instruct-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/Seed-OSS-36B-Instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantTrio/Seed-OSS-36B-Instruct-AWQ

SGLang

How to use QuantTrio/Seed-OSS-36B-Instruct-AWQ 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 "QuantTrio/Seed-OSS-36B-Instruct-AWQ" \
    --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": "QuantTrio/Seed-OSS-36B-Instruct-AWQ",
		"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 "QuantTrio/Seed-OSS-36B-Instruct-AWQ" \
        --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": "QuantTrio/Seed-OSS-36B-Instruct-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantTrio/Seed-OSS-36B-Instruct-AWQ with Docker Model Runner:
```
docker model run hf.co/QuantTrio/Seed-OSS-36B-Instruct-AWQ
```

JunHowie commited on Sep 15, 2025

Commit

e7a05d5

verified ·

1 Parent(s): dd8b5d7

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -0

README.md CHANGED Viewed

@@ -39,6 +39,13 @@ vllm serve \
 ```
 ### 【Dependencies / Installation】
 As of **2025-08-21**, create a fresh Python environment and run:
 ```bash
@@ -49,6 +56,9 @@ pip install git+https://github.com/Fazziekey/transformers.git@seed-oss
 ### 【Logs】
 ```
 2025-08-21
 1. Initial commit
 ```

 ```
 ### 【Dependencies / Installation】
+As of **2025-09-15**, create a fresh Python environment and run:
+```bash
+vllm>=0.10.2
+transformers>=4.56.1
+```
 As of **2025-08-21**, create a fresh Python environment and run:
 ```bash
 ### 【Logs】
 ```
+2025-09-15
+1.Update 【Dependencies / Installation】
 2025-08-21
 1. Initial commit
 ```