Official

meta / meta-llama-3-8b-instruct

An 8 billion parameter language model from Meta, fine tuned for chat completions

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  • 362.5M runs
  • Priced per token
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  • Prediction

    meta/meta-llama-3-8b-instruct
    ID
    855g9wxd7hrgp0cf7tsv1ewzgc
    Status
    Succeeded
    Source
    Web
    Total duration
    Created

    Input

    top_p
    0.95
    prompt
    Johnny has 8 billion parameters. His friend Tommy has 70 billion parameters. What does this mean when it comes to speed?
    temperature
    0.7
    system_prompt
    You are a helpful assistant
    length_penalty
    1
    max_new_tokens
    512
    stop_sequences
    <|end_of_text|>,<|eot_id|>
    prompt_template
    <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
    presence_penalty
    0

    Output

    The number of parameters in a neural network can impact its speed, but it's not the only factor. In general, a larger number of parameters can lead to: 1. Increased computational complexity: More parameters mean more calculations are required to process the data. 2. Increased memory requirements: Larger models require more memory to store their parameters, which can impact system performance. However, it's worth noting that the relationship between the number of parameters and speed is not always linear. Other factors, such as: * Model architecture * Optimizer choice * Hyperparameter tuning can also impact the speed of a neural network. In the case of Johnny and Tommy, it's difficult to say which one's model will be faster without more information about the models themselves.
    Generated in
    Input tokens
    39
    Output tokens
    140
    Tokens per second
    92.80 tokens / second
    Time to first token

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