Instructions to use Tasfiya025/AcademicAbstractGenerator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tasfiya025/AcademicAbstractGenerator with Transformers:

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

pipe = pipeline("text-generation", model="Tasfiya025/AcademicAbstractGenerator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tasfiya025/AcademicAbstractGenerator")
model = AutoModelForCausalLM.from_pretrained("Tasfiya025/AcademicAbstractGenerator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tasfiya025/AcademicAbstractGenerator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tasfiya025/AcademicAbstractGenerator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tasfiya025/AcademicAbstractGenerator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Tasfiya025/AcademicAbstractGenerator

SGLang

How to use Tasfiya025/AcademicAbstractGenerator 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 "Tasfiya025/AcademicAbstractGenerator" \
    --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": "Tasfiya025/AcademicAbstractGenerator",
		"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 "Tasfiya025/AcademicAbstractGenerator" \
        --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": "Tasfiya025/AcademicAbstractGenerator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Tasfiya025/AcademicAbstractGenerator with Docker Model Runner:
```
docker model run hf.co/Tasfiya025/AcademicAbstractGenerator
```

AcademicAbstractGenerator

File size: 458 Bytes

8cd3eb2

```json
{
  "results": [
    {
      "metrics": [
        {
          "name": "Perplexity (Academic Test Set)",
          "type": "perplexity",
          "value": 15.2
        }
      ],
      "models": [
        {
          "name": "AcademicAbstractGenerator",
          "sha": "0p9o8n7m6l5k4j3i2h1g0fedsdsaq"
        }
      ],
      "task": {
        "name": "Text Generation",
        "type": "text-generation"
      }
    }
  ],
  "runtime": "pytorch"
}