banhcarrot/my-qwen-lora-cisco

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Oct 3, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The banhcarrot/my-qwen-lora-cisco model is a 1.5 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. It features a substantial 32,768 token context length and is specifically optimized for Cisco-related assistant tasks, providing specialized knowledge for networking queries. This model is designed for direct use with full merged weights, requiring no PEFT adapter, and excels at answering questions about Cisco products, configurations, and troubleshooting.

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Overview

The banhcarrot/my-qwen-lora-cisco model is a specialized 1.5 billion parameter language model, derived from Qwen/Qwen2.5-1.5B-Instruct. It has been fine-tuned using LoRA/QLoRA methods to enhance its performance on Cisco-related assistant tasks. This model provides full merged weights, meaning it can be loaded directly without requiring PEFT adapters, simplifying deployment.

Key Capabilities

  • Cisco-domain Expertise: Optimized for answering questions related to Cisco products, network configurations, and troubleshooting scenarios.
  • High Context Length: Features a 32,768 token context window, allowing for processing extensive technical documentation or complex queries.
  • Direct Deployment: As a fully merged model, it integrates seamlessly with transformers and is compatible with inference servers like vLLM and TGI, as well as Hugging Face Inference Endpoints.

Good For

  • Technical Assistance: Ideal for developing AI assistants focused on Cisco networking support.
  • Knowledge Retrieval: Useful for quickly extracting information or generating responses concerning Cisco technologies.
  • Prototyping: Suitable for rapid development of domain-specific applications where a smaller, specialized model is advantageous.

Limitations

As a 1.5B parameter model, it may exhibit weaker reasoning compared to larger models and occasional factual inaccuracies regarding specific Cisco SKUs or IOS versions. Its fine-tuning may also have reduced its general-purpose capabilities. It is not intended for production-grade safety- or compliance-critical networking decisions, and all outputs should be verified.

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