abacusai/Llama-3-Giraffe-70B-Instruct

TEXT GENERATIONConcurrency Cost:4Model Size:70BQuant:FP8Ctx Length:8kPublished:May 3, 2024License:llama3Architecture:Transformer0.0K Cold

Llama-3-Giraffe-70B-Instruct by Abacus.AI is a 70 billion parameter instruction-tuned language model based on the Llama 3 architecture. It features an extended effective context length of approximately 128k tokens, achieved through PoSE and dynamic-NTK interpolation. This model is optimized for handling very long contexts while maintaining performance on instruct tasks, making it suitable for applications requiring extensive document understanding or conversational memory.

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Llama-3-Giraffe-70B-Instruct: Extended Context Llama 3

Abacus.AI presents Llama-3-Giraffe-70B-Instruct, an instruction-tuned variant of the Llama 3 70B model, specifically engineered for significantly longer context windows. This model boasts an effective context length of approximately 128k tokens, a substantial increase over its base model.

Key Capabilities & Features

  • Extended Context Window: Achieves an effective context of ~128k tokens, enabling processing of very long documents and conversations.
  • Context Extension Methodology: Utilizes Positional Skip-wise Training (PoSE) with 5 chunks and a max position ID of 32768, combined with dynamic-NTK interpolation (scale factor 4).
  • Performance Preservation: MT-Bench evaluations indicate that the context extension process did not significantly degrade performance on instruct tasks, with scores closely matching the base Meta-Llama-3-70B-Instruct.
  • Training Data: Trained on approximately 1.5 billion tokens, using ~8K long samples from the RedPajama dataset.
  • Adapter Transfer: The context extension techniques were first applied to Llama-3-70B-Base using LoRA on Q and K weights, then transferred to Llama-3-70B-Instruct.

Ideal Use Cases

  • Long Document Analysis: Excellent for tasks requiring understanding and summarization of extensive texts, such as legal documents, research papers, or books.
  • Extended Conversational AI: Suitable for chatbots or virtual assistants that need to maintain context over very long dialogues.
  • Information Retrieval: Can process large amounts of information to extract specific details or answer complex queries from vast knowledge bases.

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