cobrokerai/llama-3-1-8b

Hugging Face
TEXT GENERATIONPricing:Input $0.2 / Cached $0.028 / Output $0.32Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 31, 2024License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Warm

The cobrokerai/llama-3-1-8b is an 8 billion parameter Llama 3.1 model, developed by cobrokerai, fine-tuned from unsloth/Meta-Llama-3.1-8B-bnb-4bit. This model was trained significantly faster using Unsloth and Huggingface's TRL library, making it efficient for various natural language processing tasks. Its optimized training process allows for rapid deployment and iteration in applications requiring a capable 8B parameter model.

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cobrokerai/llama-3-1-8b Overview

The cobrokerai/llama-3-1-8b is an 8 billion parameter language model, fine-tuned by cobrokerai. It is based on the unsloth/Meta-Llama-3.1-8B-bnb-4bit architecture, leveraging the Llama 3.1 family's capabilities.

Key Characteristics

  • Efficient Training: This model was trained with a focus on speed, utilizing Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
  • Parameter Count: With 8 billion parameters, it offers a balance between performance and computational efficiency, suitable for a range of applications.
  • License: The model is released under the Apache-2.0 license, providing flexibility for commercial and research use.

Use Cases

This model is particularly well-suited for developers and researchers who require a capable 8B parameter Llama 3.1 variant that benefits from optimized training. Its faster training methodology makes it an excellent choice for:

  • Rapid prototyping and experimentation.
  • Applications where quick iteration and deployment are crucial.
  • General natural language understanding and generation tasks where an 8B model is appropriate.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p