Instructions to use huggingtweets/_pranavnt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/_pranavnt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/_pranavnt")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/_pranavnt") model = AutoModelForCausalLM.from_pretrained("huggingtweets/_pranavnt") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use huggingtweets/_pranavnt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/_pranavnt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/_pranavnt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/_pranavnt
- SGLang
How to use huggingtweets/_pranavnt 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/_pranavnt" \ --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/_pranavnt", "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/_pranavnt" \ --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/_pranavnt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/_pranavnt with Docker Model Runner:
docker model run hf.co/huggingtweets/_pranavnt
New model from https://wandb.ai/wandb/huggingtweets/runs/3b5uv7sf
Browse files- README.md +4 -4
- pytorch_model.bin +1 -1
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README.md
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---
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language: en
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thumbnail: https://
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tags:
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- huggingtweets
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widget:
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[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/
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## Training procedure
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The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_pranavnt's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/
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At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/
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## How to use
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---
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language: en
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thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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tags:
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- huggingtweets
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widget:
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| Short tweets | 86 |
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| Tweets kept | 234 |
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[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1si2997p/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
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## Training procedure
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The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_pranavnt's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3b5uv7sf) for full transparency and reproducibility.
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At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3b5uv7sf/artifacts) is logged and versioned.
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## How to use
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pytorch_model.bin
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