jondurbin/airoboros-l2-13b-3.1.1

Hugging Face
TEXT GENERATIONPricing:Input $1.5 / Output $2.1Concurrent Unit Cost:1Model Size:13BQuant:FP8Context Size:4kLicense:llama2Architecture:Transformer0.0K Open Weights Featherless Exclusive Warm

The jondurbin/airoboros-l2-13b-3.1.1 model is an experimental 13 billion parameter language model developed by jondurbin, fine-tuned primarily on synthetic data generated by the Airoboros project. This model uses the Llama-2 chat format and focuses heavily on instruction following, rather than casual chat or roleplay. It incorporates specialized training for MathJSON generation, log information extraction, anonymization, chat introspection, multi-step instructions, and de-censorship data. Its core strength lies in its ability to follow complex instructions and perform tasks like contextual question answering, summarization, and code generation.

Loading preview...

Model Overview

jondurbin/airoboros-l2-13b-3.1.1 is an experimental language model developed by jondurbin, primarily fine-tuned using synthetic data from the Airoboros project. This version is a prompt fix release, addressing an annoying space requirement from its predecessor. It is built on the airoboros-3.1 dataset, which extends the 3.0 dataset with several specialized data types.

Key Capabilities

  • Instruction Following: Designed to excel at following complex instructions, making it a general-purpose model with a strong focus on task execution.
  • MathJSON Generation: Capable of generating MathJSON solutions for mathematical questions, facilitating integration with deterministic calculation libraries. Requires a low temperature for optimal results.
  • Context-Obedient Question Answering: Trained to ignore prior knowledge and strictly use provided context for answers, reducing hallucinations. Utilizes a specific BEGININPUT/BEGINCONTEXT/ENDINPUT/BEGININSTRUCTION/ENDINSTRUCTION format for closed-context queries.
  • Summarization: Includes training for summarizing text, using a similar contextual input format.
  • Code Generation: Can handle complex coding instructions and generate code in various languages, with an option for plain code output.
  • Agent/Function Calling: Supports generating JSON or YAML for function calls based on user input, similar to OpenAI's function calling.
  • Chain-of-Thought Reasoning: Able to provide multiple potential solutions to a problem, rank them by logical soundness, and select the most feasible answer.
  • reWOO Style Execution Planning: Can construct systematic plans for complex instructions requiring multiple tool calls, outputting a sequence of function calls and evidence references.

Good For

  • Developers needing a model for precise instruction execution.
  • Applications requiring structured mathematical output (MathJSON).
  • Use cases where strict adherence to provided context is critical to prevent hallucination.
  • Automated code generation and function call orchestration.
  • Tasks benefiting from multi-step reasoning and execution planning.

Finetunes
1 models