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

Libraries

How to use huggingtweets/itsamedevdev with Transformers:

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

pipe = pipeline("text-generation", model="huggingtweets/itsamedevdev")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("huggingtweets/itsamedevdev")
model = AutoModelForCausalLM.from_pretrained("huggingtweets/itsamedevdev")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use huggingtweets/itsamedevdev with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/huggingtweets/itsamedevdev

SGLang

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

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

Update README.md

by cherry0328 - opened Jan 5

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-2

Files changed (1) hide show

README.md +4 -2

README.md CHANGED Viewed

@@ -4,7 +4,9 @@ thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?
 tags:
 - huggingtweets
 widget:
-- text: "My dream is"
 ---
 <div class="inline-flex flex-col" style="line-height: 1.5;">
@@ -82,4 +84,4 @@ In addition, the data present in the user's tweets further affects the text gene
 For more details, visit the project repository.
-[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

 tags:
 - huggingtweets
 widget:
+- text: My dream is
+base_model:
+- openai-community/gpt2
 ---
 <div class="inline-flex flex-col" style="line-height: 1.5;">
 For more details, visit the project repository.
+[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)