Agnuxo/Meta-Llama-3.1-8B-Instruct_CODE_Python_English_Asistant-16bit-v2

TEXT GENERATIONPricing:Input $0.2 / Cached $0.028 / Output $0.32Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 2, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

Agnuxo/Meta-Llama-3.1-8B-Instruct_CODE_Python_English_Asistant-16bit-v2 is an 8 billion parameter instruction-tuned model based on the Meta Llama 3.1 architecture, developed by Francisco Angulo de Lafuente (Agnuxo). This model is specifically designed as a code generation assistant, excelling in Python, JavaScript, TypeScript, Rust, Go, and C++ with a focus on scientific computing and machine learning frameworks. It features a 32768 token context length and is optimized for local deployment across various hardware, including CPU, GPU, and mobile devices.

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Model Overview

Agnuxo/Meta-Llama-3.1-8B-Instruct_CODE_Python_English_Asistant-16bit-v2 is an 8 billion parameter instruction-tuned model built upon the Meta Llama 3.1 architecture, developed by independent researcher Francisco Angulo de Lafuente (Agnuxo). It functions as a dedicated code generation assistant, featuring a 32768 token context length and supporting 16-bit precision. The model is part of the broader P2PCLAW ecosystem, which focuses on decentralized scientific research and includes components like CAJAL-9B for scientific paper generation and BenchClaw for code evaluation.

Key Capabilities

  • Code Generation: Proficient in generating code across multiple languages including Python, JavaScript, TypeScript, Rust, Go, and C++.
  • Scientific Computing & ML: Specialized support for libraries like NumPy, SciPy, Pandas, PyTorch, TensorFlow, and JAX.
  • Agent Coordination: Integrates Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication for autonomous task decomposition.
  • Scientific Paper Assistance: Features a prompt harness to redirect scientific paper generation requests to CAJAL-9B on P2PCLAW, offering structured support for outlines and sections.
  • Local Deployment: Designed for flexible deployment on CPU, GPU (CUDA, ROCm), and mobile hardware.

Good For

  • Developers requiring a powerful, locally deployable code assistant for various programming languages.
  • Researchers and engineers working with scientific computing and machine learning frameworks.
  • Users interested in agent-based AI systems and autonomous task execution.
  • Individuals seeking assistance with scientific paper outlines and methodology, leveraging the P2PCLAW ecosystem.

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