Instructions to use li-jay-cs/Summary_PPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use li-jay-cs/Summary_PPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="li-jay-cs/Summary_PPO")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("li-jay-cs/Summary_PPO") model = AutoModelForCausalLM.from_pretrained("li-jay-cs/Summary_PPO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use li-jay-cs/Summary_PPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "li-jay-cs/Summary_PPO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "li-jay-cs/Summary_PPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/li-jay-cs/Summary_PPO
- SGLang
How to use li-jay-cs/Summary_PPO 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 "li-jay-cs/Summary_PPO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "li-jay-cs/Summary_PPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "li-jay-cs/Summary_PPO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "li-jay-cs/Summary_PPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use li-jay-cs/Summary_PPO with Docker Model Runner:
docker model run hf.co/li-jay-cs/Summary_PPO
Commit History
-
iter_count = 120
a525a48 -
iter_count = 80
e3f3a7e -
iter_count = 40
5a36a6a -
iter_count = 40
671d3bb -
iter_count = 80
38a0db6 -
iter_count = 40
d147f6e -
iter_count = 40
153723a
-
iter_count = 2000
7b1e87c -
iter_count = 1600
3048dfc -
iter_count = 1200
e032105 -
iter_count = 800
3523377 -
iter_count = 800
da1fbc0 -
iter_count = 400
cae9faa -
iter_count = 400
ac0efa8 -
iter_count = 2000
903833e -
iter_count = 2000
d4a5c8d -
iter_count = 1600
0d5e9a7 -
iter_count = 1200
9749a27 -
iter_count = 800
e5a90e0 -
iter_count = 800
b240635 -
iter_count = 400
fe7d336 -
iter_count = 400
1d3157b
-
iter_count = 2000
5d1d644 -
iter_count = 2000
bd7191e -
iter_count = 1600
57c9da0 -
iter_count = 1600
0bb8601 -
iter_count = 1200
28e1cf1 -
iter_count = 1200
ede049b -
iter_count = 800
972b1d5 -
iter_count = 800
5110d28 -
iter_count = 400
7199c60 -
iter_count = 400
d867f2e -
iter_count = 0
69e3187 -
iter_count = 0
4688ca5
-
iter_count = 120
cb10451 -
iter_count = 80
3a6a436 -
iter_count = 80
35dfea7 -
iter_count = 40
700816e -
iter_count = 40
8980ec9
-
Upload model
25d7570 -
Upload model
06b8e3c -
Upload model
dd5f29d -
Upload model
ffa4215 -
Upload model
179471c -
Upload model
ef8d1cb
-
Upload model
8fd6e13 -
Upload model
3ef930b -
Upload model
23cf98d -
Upload model
6cf454f
-
Upload model
e7e9abc