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tslam/g3 is a telecommunications domain-specialized language model developed by NetoAI, fine-tuned from Google's Gemma-3 27B. The model demonstrates exceptional performance across various telecom-specific tasks, providing deep domain expertise for telecommunications applications. ## Model Details ### Model Description tslam/g3 is a 27-billion parameter language model fine-tuned specifically for telecommunications industry applications. Built upon the Gemma-3 27B foundation model, it has been trained on extensive telecom-specific datasets to deliver accurate, contextually relevant responses for telecommunications protocols, network operations, and technical workflows. - **Developed by:** NetoAI - **Model type:** Large Language Model (Fine-tuned Causal LM) - **Language(s):** English (primary), with multilingual capabilities inherited from Gemma-3 - **License:** Subject to Gemma model license terms. For commercial usage, contact NetoAI at support@netoai.ai - **Finetuned from model:** Gemma-3 27B - **Parameters:** 27 billion ### Model Sources - **Repository:** https://huggingface.co/tslam/g3 - **Base Model:** Gemma-3 27B (Google) - **Developer:** NetoAI - **Contact:** support@netoai.ai ## Uses ### Direct Use tslam/g3 excels at telecommunications domain tasks including: - **Network Troubleshooting & Diagnostics:** Analyzing network issues and providing resolution guidance - **Protocol Understanding:** Expert knowledge of 3GPP, IETF, ITU, and IEEE telecommunications standards - **Configuration Generation:** Creating and validating network configurations (BGP, OSPF, QoS, etc.) - **Technical Documentation:** Understanding and generating telecommunications technical documentation - **Customer Support:** Providing expert-level responses to telecom technical queries - **RF Network Planning:** Supporting radio frequency network design and optimization - **Compliance & Standards:** Interpreting regulatory requirements and industry standards ### Downstream Use The model can be integrated into: - Telecommunications customer support chatbots and virtual assistants - Network Operations Center (NOC) automation systems - Technical documentation generation and summarization tools - Network configuration and validation platforms - Training and educational systems for telecom professionals - Automated fault detection and resolution systems - Capacity planning and network optimization tools ## Bias, Risks, and Limitations - **Domain Specialization:** Performance is optimized for telecommunications; general knowledge may be less comprehensive than general-purpose models - **Temporal Limitations:** Knowledge reflects training data and may not include the latest telecommunications standards or technologies - **Geographic/Regional Variations:** May have varying performance across different regional telecom standards and practices - **Technical Complexity:** Outputs require validation by qualified telecom professionals for production deployment - **Training Data Bias:** May reflect biases present in telecommunications industry documentation and datasets ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Model setup model_name = "tslam/g3" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto" ) # Example: Telecom technical query prompt = "Explain the 5G handover process and key signaling procedures." messages = [ {"role": "system", "content": "You are an expert telecommunications assistant."}, {"role": "user", "content": prompt} ] # Apply chat template input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(input_text, return_tensors="pt").to(model.device) # Generate response outputs = model.generate( **inputs, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.9 ) # Decode response response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True) print(response) ``` ## Training Details ### Training Data tslam/g3 was fine-tuned on comprehensive telecommunications domain datasets, including: - **Standards Documentation:** 3GPP specifications, IETF RFCs, ITU recommendations, IEEE standards - **Technical Manuals:** Network equipment documentation, configuration guides, best practices - **Operational Data:** Network logs, troubleshooting procedures, maintenance documentation - **Protocol Specifications:** Detailed telecommunications protocol documentation - **Industry Publications:** Whitepapers, technical reports, and telecommunications research - **Customer Interaction Data:** Technical support dialogues and Q&A datasets ### Training Procedure The model underwent supervised fine-tuning on telecom-specific data to adapt the Gemma-3 27B base model for telecommunications domain expertise. #### Preprocessing - Domain-specific data cleaning and filtering - Standardization of telecommunications terminology - Quality assurance for technical accuracy - Balancing across different telecom sub-domains ## Environmental Impact Carbon emissions for the fine-tuning process: - **Hardware Type:** High-performance GPU infrastructure - **Base Model:** Gemma-3 27B (pre-trained by Google) - **Fine-tuning Compute:** [Specific details available from NetoAI] - **Carbon Emissions:** Can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) ## Technical Specifications ### Model Architecture and Objective - **Architecture:** Transformer-based, inherited from Gemma-3 27B - **Parameters:** 27 billion - **Context Length:** Supports extended context (specific length based on Gemma-3 specifications) - **Objective:** Causal language modeling fine-tuned for telecommunications domain expertise - **Training Paradigm:** Supervised fine-tuning on domain-specific corpus ## Citation **BibTeX:** ```bibtex @misc{tslam-g3-2024, title={tslam/g3: A Telecommunications-Specialized Language Model}, author={NetoAI}, year={2024}, publisher={Hugging Face}, howpublished={https://huggingface.co/tslam/g3} } ``` **APA:** NetoAI. (2024). *tslam/g3: A telecommunications-specialized language model*. Hugging Face. https://huggingface.co/tslam/g3 ## Glossary - **3GPP:** 3rd Generation Partnership Project - standards organization for mobile telecommunications - **IETF:** Internet Engineering Task Force - standards organization for internet protocols - **ITU:** International Telecommunication Union - UN specialized agency for telecommunications - **BGP:** Border Gateway Protocol - routing protocol for the internet - **OSPF:** Open Shortest Path First - interior gateway routing protocol - **QoS:** Quality of Service - network resource management mechanism - **RF:** Radio Frequency - wireless communication spectrum - **NOC:** Network Operations Center - centralized location for network monitoring and management - **Fine-tuning:** Process of adapting a pre-trained model to specific domains or tasks ## More Information For additional information, commercial licensing, or enterprise support: - **Website:** NetoAI - **Contact:** support@netoai.ai - **Model Repository:** https://huggingface.co/tslam/g3 ## Model Card Authors NetoAI Team ## Model Card Contact For questions, feedback, or access requests regarding tslam/g3, please contact: **Email:** support@netoai.ai **Model Repository:** https://huggingface.co/tslam/g3