Instructions to use NasimB/gpt2-cl-length-sampling-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NasimB/gpt2-cl-length-sampling-3 with Transformers:

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

pipe = pipeline("text-generation", model="NasimB/gpt2-cl-length-sampling-3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NasimB/gpt2-cl-length-sampling-3")
model = AutoModelForCausalLM.from_pretrained("NasimB/gpt2-cl-length-sampling-3")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NasimB/gpt2-cl-length-sampling-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NasimB/gpt2-cl-length-sampling-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NasimB/gpt2-cl-length-sampling-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NasimB/gpt2-cl-length-sampling-3

SGLang

How to use NasimB/gpt2-cl-length-sampling-3 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 "NasimB/gpt2-cl-length-sampling-3" \
    --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": "NasimB/gpt2-cl-length-sampling-3",
		"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 "NasimB/gpt2-cl-length-sampling-3" \
        --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": "NasimB/gpt2-cl-length-sampling-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NasimB/gpt2-cl-length-sampling-3 with Docker Model Runner:
```
docker model run hf.co/NasimB/gpt2-cl-length-sampling-3
```

Update README.md

by cherry0328 - opened Jan 6

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-1

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -7,6 +7,8 @@ datasets:
 model-index:
 - name: gpt2-cl-length-sampling-3
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -79,4 +81,4 @@ The following hyperparameters were used during training:
 - Transformers 4.26.1
 - Pytorch 1.11.0+cu113
 - Datasets 2.13.0
-- Tokenizers 0.13.3

 model-index:
 - name: gpt2-cl-length-sampling-3
   results: []
+base_model:
+- openai-community/gpt2
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 - Transformers 4.26.1
 - Pytorch 1.11.0+cu113
 - Datasets 2.13.0
+- Tokenizers 0.13.3