Instructions to use bickett/meme-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bickett/meme-llama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bickett/meme-llama")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bickett/meme-llama") model = AutoModelForCausalLM.from_pretrained("bickett/meme-llama") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bickett/meme-llama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bickett/meme-llama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bickett/meme-llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bickett/meme-llama
- SGLang
How to use bickett/meme-llama 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 "bickett/meme-llama" \ --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": "bickett/meme-llama", "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 "bickett/meme-llama" \ --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": "bickett/meme-llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bickett/meme-llama with Docker Model Runner:
docker model run hf.co/bickett/meme-llama
Llama 2 Meme Generator
Model Description
This model is a fine-tuned version of the llama 2 model, specifically tailored for generating meme captions. It captures the essence and humor commonly found in popular internet memes and offers a unique approach to meme creation. Just provide a prompt or a meme context, and let the model generate a fitting caption!
Training Data
The model was trained using a diverse dataset of meme captions, spanning various internet trends, jokes, and pop culture references. This ensures a wide range of meme generation capabilities, from classic meme formats to contemporary internet humor.
Training Procedure
The model was fine-tuned using the autotrain llm command with optimal hyperparameters for meme generation. Special care was taken to avoid overfitting, ensuring the model can generalize well across various meme contexts.
Usage
To generate a meme caption using this model, you can use the following code:
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("bickett/meme-llama")
model = AutoModelWithLMHead.from_pretrained("bickett/meme-llama")
input_text = "When you try to code without coffee"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
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