distil-labs/distil-siemens-s7-1200-docs-llama-1b

TEXT GENERATIONPricing:Input $0.108 / Output $0.804Concurrent Unit Cost:1Model Size:1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Feb 16, 2026License:llama3.2Architecture:Transformer0.0K Featherless Exclusive Cold

distil-labs/distil-siemens-s7-1200-docs-llama-1b is a 1 billion parameter Llama 3.2 Instruct model, fine-tuned by Distil Labs for question-answering over Siemens SIMATIC S7-1200 PLC documentation. This model is specifically designed for RAG pipelines in industrial environments, matching the performance of a 3 billion parameter base model on domain-specific technical QA. It excels at providing precise answers from dense technical manuals, making it suitable for edge hardware without GPUs.

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Overview

This model is a fine-tuned Llama 3.2 1B Instruct model developed by Distil Labs, specifically optimized for question-answering on Siemens SIMATIC S7-1200 PLC documentation. It addresses the challenge of deploying LLMs in industrial environments with strict security and limited hardware resources, demonstrating that small, fine-tuned models can be highly effective.

Key Capabilities

  • Domain-Specific QA: Excels at answering technical questions from Siemens S7-1200 PLC manuals, including alarm codes, signal addresses, and parameter tables.
  • Distilled Performance: Through distillation, this 1B parameter model achieves a 61.1% pass rate on a held-out evaluation set, matching or exceeding the performance of a 3B parameter base model (60.4%).
  • Edge Deployment: Designed for use on edge hardware without requiring expensive GPUs, making it practical for on-premise industrial applications.
  • RAG Pipeline Integration: Intended to be paired with a Retrieval Augmented Generation (RAG) pipeline to provide context-aware answers.

Intended Use

This model is primarily intended for use as part of a RAG pipeline to answer technical questions based on Siemens S7-1200 documentation. It is not designed for general-purpose chat or tasks outside this specific technical domain. Its small size and specialized training make it ideal for scenarios where larger cloud-based LLMs are not feasible due to security or hardware constraints.