PericlesSavio/llama2-13b-chat-hf-finetuned

TEXT GENERATIONConcurrency Cost:1Model Size:13BQuant:FP8Ctx Length:4kArchitecture:Transformer Cold

PericlesSavio/llama2-13b-chat-hf-finetuned is a 13 billion parameter Llama 2 model developed by Meta, fine-tuned for dialogue use cases. This auto-regressive language model utilizes an optimized transformer architecture and is specifically optimized for chat applications. It is designed for commercial and research use in English, outperforming many open-source chat models on various benchmarks.

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Overview

PericlesSavio/llama2-13b-chat-hf-finetuned is a 13 billion parameter variant from Meta's Llama 2 family of large language models. This specific model is a fine-tuned version, optimized for dialogue and chat applications, and converted for the Hugging Face Transformers format. Llama 2 models were trained on 2 trillion tokens of publicly available data, with fine-tuning data including over one million new human-annotated examples.

Key Capabilities

  • Dialogue Optimization: Specifically fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety in chat scenarios.
  • Performance: Llama-2-Chat models, including this 13B variant, have shown to outperform open-source chat models on most tested benchmarks and are competitive with some popular closed-source models like ChatGPT and PaLM in human evaluations for helpfulness and safety.
  • Context Length: Features a 4k token context length, suitable for extended conversational turns.
  • Commercial Use: Licensed for both commercial and research applications.

Intended Use Cases

  • Assistant-like Chat: Ideal for building conversational AI agents and chatbots.
  • Natural Language Generation: While fine-tuned for chat, the underlying Llama 2 architecture can be adapted for various text generation tasks.

Limitations

  • English Only: Intended for use exclusively in English.
  • Safety Considerations: As with all LLMs, potential for inaccurate, biased, or objectionable responses; requires safety testing and tuning for specific applications.