Collaborative Language Model Runner Howto
Petals is an open-source system that enables collaborative inference and fine-tuning of large language models by distributing model parts across multiple users.
View MoreHow to Use Collaborative Language Model Runner
Install Petals: Install Petals and its dependencies using pip: pip install git+https://github.com/bigscience-workshop/petals
Import required modules: Import the necessary modules from Petals and Transformers: from transformers import AutoTokenizer; from petals import AutoDistributedModelForCausalLM
Choose a model: Select a large language model available on the Petals network, such as 'meta-llama/Meta-Llama-3.1-405B-Instruct'
Initialize tokenizer and model: Create the tokenizer and model objects: tokenizer = AutoTokenizer.from_pretrained(model_name); model = AutoDistributedModelForCausalLM.from_pretrained(model_name)
Prepare input: Tokenize your input text: inputs = tokenizer(prompt, return_tensors='pt')
Generate output: Use the model to generate text based on the input: outputs = model.generate(**inputs, max_new_tokens=100)
Decode output: Decode the generated token IDs back into text: generated_text = tokenizer.decode(outputs[0])
Optional: Contribute resources: To help expand the network, you can run a Petals server to share your GPU: python -m petals.cli.run_server model_name
Collaborative Language Model Runner FAQs
Petals is an open-source system that allows users to run large language models (100B+ parameters) collaboratively in a distributed manner, similar to BitTorrent. It enables running models like BLOOM-176B for inference and fine-tuning by having users load small parts of the model and teaming up with others.
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