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

Libraries

How to use huggingtweets/stdoval_ with Transformers:

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

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

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

tokenizer = AutoTokenizer.from_pretrained("huggingtweets/stdoval_")
model = AutoModelForCausalLM.from_pretrained("huggingtweets/stdoval_", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use huggingtweets/stdoval_ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/huggingtweets/stdoval_

SGLang

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

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

stdoval_ / README.md

boris

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	---
	language: en
	thumbnail: https://www.huggingtweets.com/stdoval_/1614174048334/predictions.png
	tags:
	- huggingtweets
	widget:
	- text: "My dream is"
	---

	<div>
	<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1342243892100534274/-1_pP6Do_400x400.jpg')">
	</div>
	<div style="margin-top: 8px; font-size: 19px; font-weight: 800">𝕯𝖆𝖛𝖎𝖉 𝕾𝖙. 𝕯𝖔𝖛𝖆𝖑 🤖 AI Bot </div>
	<div style="font-size: 15px">@stdoval_ bot</div>
	</div>

	I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

	Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!

	## How does it work?

	The model uses the following pipeline.

	![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

	To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

	## Training data

	The model was trained on [@stdoval_'s tweets](https://twitter.com/stdoval_).

	\| Data \| Quantity \|
	\| --- \| --- \|
	\| Tweets downloaded \| 3059 \|
	\| Retweets \| 2250 \|
	\| Short tweets \| 154 \|
	\| Tweets kept \| 655 \|

	[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/d4oj280h/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.

	## Training procedure

	The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @stdoval_'s tweets.

	Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1b6xui8t) for full transparency and reproducibility.

	At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1b6xui8t/artifacts) is logged and versioned.

	## How to use

	You can use this model directly with a pipeline for text generation:

	```python
	from transformers import pipeline
	generator = pipeline('text-generation',
	model='huggingtweets/stdoval_')
	generator("My dream is", num_return_sequences=5)
	```

	## Limitations and bias

	The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).

	In addition, the data present in the user's tweets further affects the text generated by the model.

	## About

	Built by Boris Dayma

	[![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)

	For more details, visit the project repository.

	[![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)