algoscienceacademy/Harness

TEXT GENERATIONConcurrent Unit Cost:1Model Size:1.1BQuant:BF16Context Size:2kPublished:Jul 14, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Harness-1B is a 1.1 billion parameter Llama-based causal language model developed by Algo Science Lab. It is fine-tuned for instruction following, coding assistance across multiple languages including Verilog and SystemVerilog, and specialized reasoning in semiconductor engineering, VLSI design, and mathematics. This lightweight model is optimized for local deployment and provides strong performance for technical and general AI tasks.

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Harness-1B: A Specialized 1.1B Parameter LLM

Harness-1B is a compact, 1.1 billion parameter large language model developed by Algo Science Lab. Built on a Llama-based architecture, it is specifically fine-tuned for instruction following and excels in a variety of technical domains, making it suitable for local deployment and embedded AI applications.

Key Capabilities

  • Specialized Technical Assistance: Strong performance in semiconductor engineering, VLSI design, FPGA development, ASIC design, CMOS digital design, and related fields.
  • Multi-Language Code Generation: Proficient in generating code for Python, C, C++, Rust, Java, JavaScript, TypeScript, Verilog HDL, and SystemVerilog.
  • Reasoning and Problem Solving: Designed to assist with mathematics, electronics, physics, and general problem-solving.
  • General Conversational AI: Capable of general conversation, answering questions, and explaining concepts.
  • Lightweight Deployment: With approximately 1 billion parameters and a 2048-token context length, it's optimized for local AI deployment and resource-constrained environments.

Intended Uses

Harness-1B is ideal for:

  • Programming Assistants: Generating and debugging code, explaining algorithms.
  • Engineering Support: Assisting with electronics design, FPGA/ASIC/VLSI workflows, and RTL development.
  • Educational Applications: Explaining technical concepts and solving problems in STEM fields.
  • AI Chatbots: Providing technical and general conversational capabilities.
  • Local and Embedded AI: Its small size makes it suitable for deployment on devices with limited resources, including GGUF format support for llama.cpp, LM Studio, and other local inference engines.

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