Instructions to use Ateeqq/keywords-title-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ateeqq/keywords-title-generator with Transformers:

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

pipe = pipeline("text-generation", model="Ateeqq/keywords-title-generator")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Ateeqq/keywords-title-generator")
model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/keywords-title-generator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ateeqq/keywords-title-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Ateeqq/keywords-title-generator

SGLang

How to use Ateeqq/keywords-title-generator 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 "Ateeqq/keywords-title-generator" \
    --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": "Ateeqq/keywords-title-generator",
		"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 "Ateeqq/keywords-title-generator" \
        --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": "Ateeqq/keywords-title-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ateeqq/keywords-title-generator with Docker Model Runner:
```
docker model run hf.co/Ateeqq/keywords-title-generator
```

Ateeqq commited on Apr 8, 2025

Commit

8b3826b

verified ·

1 Parent(s): 7dc7918

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -15

README.md CHANGED Viewed

@@ -1,5 +1,5 @@
 ---
-license: openrail
 inference:
   parameters:
     temperature: 0.7
@@ -13,15 +13,12 @@ pipeline_tag: text2text-generation
 tags:
 - text-generation-inference
 widget:
-  - text: >-
-      generate title: Importance, Dataset, AI
-    example_title: Example 1
-  - text: >-
-      generate title: Amazon, Product, Business
-    example_title: Example 2
-  - text: >-
-      generate title: History, Computer, Software
-    example_title: Example 3
 ---
 # Generate Title using Keywords
@@ -75,8 +72,4 @@ generate_title(keywords)
  'Using fine tuning to fine-tune your machine learning model',
  'Machine Learning: Fine-tuning your model to fit the needs of machine learning',
  'The Art of Fine-Tuning Your Machine Learning Model']
-```
-### Disclaimer:
-It grants a non-exclusive, non-transferable license to use the this model. This means you can't freely share it with others or sell the model itself. However you can use the model for commercial purposes.

 ---
+license: apache-2.0
 inference:
   parameters:
     temperature: 0.7
 tags:
 - text-generation-inference
 widget:
+- text: 'generate title: Importance, Dataset, AI'
+  example_title: Example 1
+- text: 'generate title: Amazon, Product, Business'
+  example_title: Example 2
+- text: 'generate title: History, Computer, Software'
+  example_title: Example 3
 ---
 # Generate Title using Keywords
  'Using fine tuning to fine-tune your machine learning model',
  'Machine Learning: Fine-tuning your model to fit the needs of machine learning',
  'The Art of Fine-Tuning Your Machine Learning Model']
+```