Instructions to use a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("openai/gpt-oss-20b")
model = PeftModel.from_pretrained(base_model, "a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128")

Transformers

How to use a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128 with Transformers:

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

pipe = pipeline("text-generation", model="a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128

SGLang

How to use a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128 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 "a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128" \
    --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": "a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128",
		"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 "a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128" \
        --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": "a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128 with Docker Model Runner:
```
docker model run hf.co/a3ilab-llm-uncertainty/gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128
```

gptoss_20b_all_zhtw_lr5e-7_ep1_16_64_128

File size: 209 Bytes

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{
    "epoch": 1.0,
    "total_flos": 1.1837248712042471e+19,
    "train_loss": 1.1698329736722395,
    "train_runtime": 12991.4192,
    "train_samples_per_second": 1.463,
    "train_steps_per_second": 0.011
}