aws-neuron/zephyr-7b-seqlen-2048-bs-4-cores-2

Neuronx model for Zephyr 7B β

This repository contains AWS Inferentia2 and neuronx compatible checkpoints for HuggingFaceH4/zephyr-7b-beta. You can find detailed information about the base model on its Model Card.

This model has been exported to the neuron format using specific input_shapes and compiler parameters detailed in the paragraphs below.

Please refer to the 🤗 optimum-neuron documentation for an explanation of these parameters.

Usage on Amazon SageMaker

coming soon

Usage with 🤗 optimum-neuron

from optimum.neuron import pipeline

pipe = pipeline('text-generation', 'aws-neuron/zephyr-7b-seqlen-2048-bs-4-cores-2')
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

This repository contains tags specific to versions of neuronx. When using with 🤗 optimum-neuron, use the repo revision specific to the version of neuronx you are using, to load the right serialized checkpoints.

Arguments passed during export

input_shapes

{
  "batch_size": 4,
  "sequence_length": 2048,
}

compiler_args

{
  "auto_cast_type": "fp16",
  "num_cores": 2,
}