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README.md

Llama 3.1 is the latest iteration of the Llama series, an advanced AI language model developed to understand and generate human-like text. It leverages state-of-the-art machine learning techniques to provide high-quality, context-aware responses.

Key Features

  • Enhanced Natural Language Understanding (NLU): Llama 3.1 has significantly improved NLU capabilities, enabling it to comprehend and process complex language patterns more effectively.
  • Contextual Awareness: It maintains context over extended conversations, ensuring coherent and relevant responses even in lengthy interactions.
  • Multilingual Support: Llama 3.1 supports multiple languages, making it versatile for global applications.
  • Customizability: Developers can fine-tune the model on specific datasets to cater to niche requirements, enhancing its adaptability.

Technical Specifications

  • Architecture: Transformer-based architecture with advanced attention mechanisms.
  • Parameters: The model contains billions of parameters, contributing to its high performance in various NLP tasks.
  • Training Data: Trained on a diverse corpus of text, including books, articles, and websites, to ensure a broad understanding of language.
  • Fine-tuning: Supports fine-tuning on domain-specific data to improve performance in targeted applications.

Performance Metrics

  • Accuracy: Achieves high accuracy in language comprehension and generation benchmarks.
  • Latency: Optimized for low-latency responses, ensuring real-time interaction capabilities.
  • Scalability: Designed to scale efficiently across multiple servers, supporting high-demand applications.

Use Cases

  • Chatbots and Virtual Assistants: Provides human-like interactions, enhancing user experience.
  • Content Generation: Assists in creating articles, reports, and other textual content.
  • Language Translation: Offers accurate and context-aware translations across supported languages.
  • Customer Support: Automates responses to common queries, improving support efficiency.

Serve with vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving.

vLLM is fast with:

  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Fast model execution with CUDA/HIP graph
  • Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
  • Optimized CUDA kernels

vLLM is flexible and easy to use with:

  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA GPUs and AMD GPUs
  • (Experimental) Prefix caching support
  • (Experimental) Multi-lora support

vLLM seamlessly supports many Hugging Face models, including the following architectures:

  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • Command-R (CohereForAI/c4ai-command-r-v01, etc.)
  • DBRX (databricks/dbrx-base, databricks/dbrx-instruct etc.)
  • DeciLM (Deci/DeciLM-7B, Deci/DeciLM-7B-instruct, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • Gemma (google/gemma-2b, google/gemma-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • InternLM2 (internlm/internlm2-7b, internlm/internlm2-chat-7b, etc.)
  • Jais (core42/jais-13b, core42/jais-13b-chat, core42/jais-30b-v3, core42/jais-30b-chat-v3, etc.)
  • LLaMA, Llama 2, and Meta Llama 3 (meta-llama/Meta-Llama-3-8B-Instruct, meta-llama/Meta-Llama-3-70B-Instruct, meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • MiniCPM (openbmb/MiniCPM-2B-sft-bf16, openbmb/MiniCPM-2B-dpo-bf16, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, mistral-community/Mixtral-8x22B-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OLMo (allenai/OLMo-1B-hf, allenai/OLMo-7B-hf, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Orion (OrionStarAI/Orion-14B-Base, OrionStarAI/Orion-14B-Chat, etc.)
  • Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
  • Phi-3 (microsoft/Phi-3-mini-4k-instruct, microsoft/Phi-3-mini-128k-instruct, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Qwen2 (Qwen/Qwen1.5-7B, Qwen/Qwen1.5-7B-Chat, etc.)
  • Qwen2MoE (Qwen/Qwen1.5-MoE-A2.7B, Qwen/Qwen1.5-MoE-A2.7B-Chat, etc.)
  • StableLM(stabilityai/stablelm-3b-4e1t, stabilityai/stablelm-base-alpha-7b-v2, etc.)
  • Starcoder2(bigcode/starcoder2-3b, bigcode/starcoder2-7b, bigcode/starcoder2-15b, etc.)
  • Xverse (xverse/XVERSE-7B-Chat, xverse/XVERSE-13B-Chat, xverse/XVERSE-65B-Chat, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)

Getting Started

Visit our documentation to get started.