mindfossil/telecom-intelligence-model-v6-merged

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 28, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

mindfossil/telecom-intelligence-model-v6-merged is a 7.6 billion parameter language model fine-tuned from Qwen/Qwen2.5-7B-Instruct, specifically optimized for telecom network operations. It excels at root cause analysis, KPI degradation diagnosis, and generating canonical commands across Ericsson, Huawei, and Nokia RAN/Core nodes. With a 32768 token context length, it provides precise answers for tasks like PRB utilization calculation, SON Energy Saving decisions, and 5G Core Network Function attribution.

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Telecom Intelligence Model v6

This model, mindfossil/telecom-intelligence-model-v6-merged, is a 7.6 billion parameter language model built upon Qwen/Qwen2.5-7B-Instruct. It has been extensively fine-tuned using QLoRA (4-bit) and SFT via Unsloth, specifically for the domain of telecom network operations. The model is designed to reason step-by-step over complex telecom operational data, providing highly specialized insights.

Key Capabilities

  • Root Cause Analysis: Diagnoses KPI degradations from PM counter data across Ericsson, Huawei, and Nokia RAN/Core nodes.
  • PRB Utilisation: Accurately computes DL/UL PRB utilisation percentages for all LTE bandwidths and 5G NR.
  • SON Energy Saving: Evaluates ES cell switch-off conditions and provides binary ACTIVATE/DO NOT ACTIVATE decisions.
  • Multi-vendor Normalisation: Maps vendor-specific counter names (Ericsson, Huawei, Nokia) to equivalent KPI formulas.
  • Huawei MML: Generates canonical Huawei MML commands using correct verbs.
  • 5G Core NF Attribution: Identifies exact failing Network Functions (AMF, SMF, UPF, etc.) and interfaces.
  • Specialized Domains: Covers IMS/VoNR, O-RAN, 3GPP Rel-17 NTN LEO satellite, and Cloud-native NF issues.

Smart Query Routing

The model performs optimally when provided with a domain-specific system prompt. A recommended ask() wrapper automatically classifies queries (e.g., PRB, MML, SON, General) and applies the appropriate system prompt for enhanced accuracy and relevance.

Performance

Version 6 achieves an 18/20 score on a 20-question domain-specific evaluation, demonstrating strong performance across critical telecom tasks like PRB utilisation, Huawei MML, SON ES decisions, and 5GC NF attribution. It was trained on 694 examples across 45 batches, focusing on diverse telecom scenarios.

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