jondurbin/airoboros-l2-7b-3.0

Hugging Face
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7BQuant:FP8Context Size:4kLicense:llama2Architecture:Transformer0.0K Open Weights Featherless Exclusive Warm

The jondurbin/airoboros-l2-7b-3.0 is a 7 billion parameter experimental language model developed by jondurbin, built on the Llama-2 architecture with a 4096-token context length. It is fine-tuned primarily on synthetic data from the airoboros-3.0 dataset, focusing heavily on instruction following rather than casual chat. This model incorporates unique features like MathJSON for deterministic calculations and enhanced context-obedient question answering.

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Overview

The jondurbin/airoboros-l2-7b-3.0 is an experimental 7B parameter model built on the Llama-2 architecture, fine-tuned by jondurbin using the airoboros-3.0 dataset. This iteration updates the prompt format to Llama-2 chat style and emphasizes strong instruction following over casual conversation. It integrates novel features like MathJSON for precise mathematical operations and includes human-generated multi-turn roleplay data to improve conversational coherence.

Key Capabilities

  • Instruction Following: Heavily focused on accurate and robust instruction execution.
  • MathJSON Integration: Supports deterministic mathematical calculations via a structured JSON output, designed to be parsed and executed by external tools like CortexJS Compute Engine.
  • Context-Obedient QA: Trained to strictly adhere to provided context for question answering, minimizing hallucinations.
  • Summarization: Capable of summarizing provided text inputs using a specific closed-context format.
  • Code Generation: Generates code based on complex requirements, including multi-threaded servers and web applications.
  • Agent/Function Calling: Can generate JSON or YAML for function calls based on user input, similar to OpenAI's function calling.
  • Chain-of-Thought Reasoning: Provides multiple potential solutions, ranks them, and selects the most feasible answer for complex problems.
  • reWOO-style Execution Planning: Supports generating systematic plans for multi-tool execution based on a set of available functions.

Good For

  • Applications requiring precise instruction following and deterministic outputs.
  • Tasks involving mathematical problem formulation and external computation.
  • Closed-context question answering where hallucination reduction is critical.
  • Generating structured outputs for agentic workflows and function calling.
  • Complex code generation and summarization tasks.

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