--- license: apache-2.0 base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit tags: - telecom - ericsson - huawei - nokia - zte - 5g - 5g-nr - lte - ran - 5gc - oran - fine-tuned - qwen2.5 - unsloth language: - en library_name: transformers pipeline_tag: text-generation model-index: - name: telecom-intelligence-model-v7-merged results: [] --- # Telecom Intelligence — Qwen2.5-7B v7 A domain-fine-tuned LLM for telecom network operations, built on [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using QLoRA (4-bit) + SFT via [Unsloth](https://github.com/unslothai/unsloth). This is the **merged, standalone model** — no adapter loading required. Compatible with vLLM, Transformers, and HuggingFace Inference Endpoints. **v7 adds 200 new training examples** (805 total) covering 5G NR deep internals, multi-vendor PM counter diagnosis, and advanced 5GC NF fault chains — the largest training set in this series. --- ## What it does The model reasons step-by-step over telecom operational data to: - **Root cause analysis** — diagnose KPI degradations from PM counter data across Ericsson, Huawei, Nokia, and ZTE RAN/Core nodes - **5G NR deep knowledge** — SSB beam management (P1/P2/P3/BFR), NR numerology (μ=0–4, SCS, slot duration, PRBs per bandwidth), CORESET/PDCCH blind decoding, BWP switching, PUCCH formats 0–4 (HARQ-ACK/SR/CSI), SIB1 contents, F1/E1 interface (CU/DU split), RRC_INACTIVE state (I-RNTI, RNA, Resume), SDAP layer (QoS flow→DRB mapping) - **LTE RAN** — RRC/ERAB/HO KPI chains with correct Ericsson `pmRrcConnEstabSucc`/`pmErabEstabSuccInit`/`pmHoExeSuccLteIntraF` formulas; inter-frequency HO (A2/A3/A4/A5 events, measurement gaps); LTE TA/TAU; eICIC/ABS (HetNet, CRE, FeICIC); S1 release causes; RSRP→SINR→CQI→MCS link adaptation chain with OLLA - **5G Core NF attribution** — names the exact failing NF (AMF, SMF, UPF, PCF, AUSF, UDM) and interface (N4/PFCP, N8, N11, N7, N10, NGAP) rather than vague "core network" answers; AMF overload scenarios, SMF/PFCP session failure RCA, UPF pod crash diagnosis - **PRB utilisation** — correct formula for all LTE bandwidths and 5G NR; differentiates congestion vs RF root cause - **SON Energy Saving** — binary ACTIVATE / DO NOT ACTIVATE decisions with threshold reasoning (PRB, UE count, neighbour overlap, NOC approval) - **Multi-vendor counter normalisation** — Ericsson `pm*`, Huawei `L.*` / `VS.5G.*`, Nokia `RRC_CONN_*`, ZTE LTE PM naming conventions; maps all to equivalent KPI formulas - **Huawei MML** — canonical command verbs: `BLK`/`UBL`, `LST`, `DSP`, `MOD`, `RST`, `ACT`, `DEA`, `ADD`, `RMV`; includes `RST NRDUCELL` (5G DU cell reset procedure) - **Ericsson AMOS CLI** — `get`/`set`/`la`/`st` commands; targets correct MO classes (`EUtranCellFDD`, `NRCellDU`, `AntennaUnitGroup`, `RetSubUnit`); uses `administrativeState` attribute - **EN-DC / NR-DC / NSA vs SA** — architecture differences, X2/Xn procedures, EN-DC setup failure diagnosis, UE capability procedure - **PDCP lossless handover** — SN Status Transfer, PDCP SDU forwarding, in-sequence delivery guarantee - **A3 event HO parameters** — a3-Offset, hysteresis, TTT, MRO; UL power control (P0/alpha/TPC); HSDPA vs LTE L2 scheduling differences - **DL spectral efficiency** — derived from `pmPdcpVolDlDrb` + `pmPrbUsedDlSum` with correct 18 MHz used BW for 20 MHz LTE - **ENM vs ENIQ analytics** — when to use each, ENIQ SQL example, KPI cooking vs ad-hoc troubleshooting - **Nokia NetAct 5G** — RACT CLI, BTS Manager CLI, MO hierarchy (GNBDU/GNBCUCP/GNBCUUP), PM counter access - **O-RAN, VoLTE/VoNR, IMS, NTN, cloud-native NF** — coverage inherited from v5/v6 --- ## Training details | Parameter | Value | |-----------|-------| | Base model | `unsloth/Qwen2.5-7B-Instruct-bnb-4bit` | | Training examples | 805 | | Training method | QLoRA (4-bit) + SFT via Unsloth + TRL SFTTrainer | | LoRA rank | r=16, alpha=32 | | LoRA target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj | | Max sequence length | 2048 | | Epochs | 3 | | Merge type | 16-bit merged (standalone, no adapter required) | --- ## Training data coverage New in v7 (batches 47–52, ~110 examples): - LTE TA/TAU, S6a vs N8 interface distinction - Ericsson AMOS commands for LTE and 5G NR - RSRP vs RSRQ — measurement purpose and thresholds - `pmRrcConnEstabFail` by cause code - LTE SCell/CA activation, UE power headroom (PHR) - Huawei iMaster NCE vs U2020 differences - 5G NSA vs SA architecture, EN-DC procedure + counters - AMF overload scenarios and handling - Huawei `VS.5G.*` PM counter families - LTE S1 UE Context Release causes - ZTE LTE PM counter naming convention - 5G network slicing / S-NSSAI - LTE inactivity timer + DRX interaction - LTE X2 interface procedures - LTE UL power control (P0, alpha, TPC) - PDCP lossless HO (SN Status Transfer + forwarding) - A3 event parameters (a3-Offset, hysteresis, TTT, MRO) - eNB vs gNB CU/DU split (F1/E1 interfaces) - WCDMA HSDPA vs LTE L2/scheduling architecture - Nokia GNBCUCP HO counter hierarchy and dashboard - 5G NR PRB high + low throughput diagnosis workflow - NR-DC vs EN-DC differences - 5G NR physical channels and signals (PSS/SSS/PBCH/PDCCH/PDSCH/PUCCH/PRACH/SRS) - LTE E2E session — power-on to first packet (22 steps) - Ericsson `pmErabEstabSuccInitMmeTrigger` (MME-triggered E-RAB) - 5G NR CORESET + PDCCH blind decoding - 5G BWP concept and switching - SSB beam management (P1/P2/P3, BFR) - NGAP message set (NG Setup, InitialUEMessage, PDU session, HO, Paging, Reset) - LTE UE category table (Cat-1 to Cat-NB1, peak rates) - WCDMA R99 AMR vs VoLTE/VoNR evolution - 5G UPF failure scenarios - 5G NR numerology (μ=0–4, SCS implications) - Huawei SmartPower (Symbol/Channel/Cell Shutdown, energy saving counters) - LTE inter-freq HO (A2/A3/A4/A5/A1 events, measurement gaps) - GTP-U protocol (TEID, protocol stack, LTE vs 5G N3 with QFI) - WCDMA soft HO vs LTE hard HO (fundamental CDMA vs OFDMA reason) - RRC drop diagnosis (congestion/PRACH/MME) - SMF role + N4/N7/N10/N11/N40 interfaces - NR RACH 4-step vs 2-step (Type 1 vs Type 2, SSB association) - SDAP layer (QoS flow→DRB mapping, Reflective QoS, CU-UP) - SRS and massive MIMO UL (reciprocity-based beamforming, MU-MIMO scheduling) - Ericsson top 5 5G alarms + actions - 3GPP Rel-17 key features (RedCap, NTN, Sidelink, Positioning, MBS) - LTE DL spectral efficiency calculation from PM counters - 5G NR PUCCH (formats 0–4, UCI: HARQ/SR/CSI) - Ericsson `pmHoPrepSuccLteIntraF` + full HO KPI chain - Ericsson ENIQ vs ENM PM — analytics use-case comparison - Nokia NetAct 5G management (RACT CLI, MO hierarchy, PM counters) - 5G RRC_INACTIVE vs RRC_CONNECTED (resume, I-RNTI, small data) - Ericsson paging SR% (pmPagingAtt/Succ, DRX, TAU) - 5G NR SIB1 contents (cellAccessRelatedInfo, RACH config, TDD offset) - LTE eICIC/ABS (HetNet, ABS bitmap, FeICIC, CRE) - LTE UE capability procedure (SupportedBandCombinationList) - VS.5G.Cell.HO.Fail.RLF — antenna replacement root cause diagnosis - L.HO.Succ.Inter.eNB vs Intra.eNB + X2 vs S1 HO breakdown - 5G F1 interface (F1AP message set, F1-U GTP-U) - Huawei MML `RST NRDUCELL` (cell reset procedure + precautions) - RSRP→SINR→CQI→MCS link adaptation chain (CQI table, OLLA) --- ## Quick start ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "mindfossil/telecom-intelligence-model-v7-merged" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto", ) SYSTEM_PROMPT = ( "You are a telecom network intelligence assistant. " "You analyse probe data, RAN PM counters, Core PM metrics, and transport layer KPIs " "to detect anomalies, diagnose faults, perform root cause analysis, translate natural " "language to queries, and generate AMOS or MML CLI commands. " "Always reason step by step: identify the vendor and counter naming convention, " "compute all KPIs explicitly showing the arithmetic, compare against known thresholds, " "then state the root cause and recommended action." ) def ask(question: str) -> str: messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": question}, ] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" ).to(model.device) outputs = model.generate( inputs, max_new_tokens=1024, temperature=0.1, do_sample=True ) return tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True) # Example print(ask( "Ericsson cell ENB010-CELL01: pmRrcConnEstabAtt=948, pmRrcConnEstabSucc=921, " "pmErabEstabAttInit=921, pmErabEstabSuccInit=902, pmHoExeAttLteIntraF=421, " "pmHoExeSuccLteIntraF=412. Analyse for anomalies." )) ``` --- ## HuggingFace Inference Endpoint (recommended for production) Deploy as a dedicated endpoint for low-latency inference without managing GPU infrastructure: ```python import requests, json, os ENDPOINT_URL = "https://.aws.endpoints.huggingface.cloud" HF_TOKEN = os.environ["HF_TOKEN"] def ask_endpoint(question: str, system: str = None) -> str: system = system or ( "You are a telecom network intelligence assistant. Reason step by step, " "derive all KPIs showing arithmetic, identify the vendor and counter naming, " "compare against thresholds, then state the root cause and recommended action." ) payload = { "model": "mindfossil/telecom-intelligence-model-v7-merged", "messages": [ {"role": "system", "content": system}, {"role": "user", "content": question}, ], "max_tokens": 1500, "temperature": 0.1, } r = requests.post( f"{ENDPOINT_URL}/v1/chat/completions", headers={"Authorization": f"Bearer {HF_TOKEN}", "Content-Type": "application/json"}, json=payload, timeout=180, ) r.raise_for_status() return r.json()["choices"][0]["message"]["content"] ``` --- ## vLLM (self-hosted) ```bash pip install vllm vllm serve mindfossil/telecom-intelligence-model-v7-merged \ --dtype float16 \ --max-model-len 4096 \ --gpu-memory-utilization 0.90 ``` --- ## Example prompts **PM counter anomaly detection:** ``` TASK: Anomaly Detection VENDOR: Ericsson | Cell: ENB042-CELL07 | Granularity: 15 min pmRrcConnEstabAtt=941, pmRrcConnEstabSucc=908 pmErabEstabAttInit=941, pmErabEstabSuccInit=824 pmHoExeAttLteIntraF=378, pmHoExeSuccLteIntraF=288 Analyse for anomalies. Derive all KPIs showing your arithmetic, then state the verdict. ``` **5G NR beam failure diagnosis:** ``` After an antenna upgrade at a 5G NR cell, UEs near the cell edge show intermittent disconnections. Logs show frequent BFR (Beam Failure Recovery) events. Explain the SSB beam management procedure (P1/P2/P3), what triggers BFR, and which PM counters to check. ``` **5GC NF root cause:** ``` SMF-PROD-03: PDU_Session_Estab_SR=58.3% (baseline 99.1%), N4_HeartbeatTimeout_Rate=22.1% (baseline 0%), N11_SR=99.8%, N7_SR=99.6%, SMF_CPU_Util=41%. Diagnose the root cause. Which NF is failing and on which interface? ``` **Ericsson AMOS CLI:** ``` TASK: AMOS CLI command generation VENDOR: Ericsson | Node: GNBDU-SITE-05 Generate AMOS commands to: 1. Lock all NRCellDU cells (administrativeState) 2. Retrieve pmNrRrcConnEstabSucc and pmNrRrcConnEstabAtt for all cells 3. Unlock NRCellDU=Cell-1 ``` --- ## Limitations - Output quality depends on how explicitly vendor, counter names, and task type are stated in the prompt. The structured prompt format shown above consistently outperforms free-form questions. - The model was not trained on proprietary network configurations or live traffic data. It reasons from 3GPP specifications and publicly available vendor documentation. - Computed KPI values are arithmetic derivations from counter inputs provided in the prompt — the model does not connect to live network systems. - Recommended for augmenting, not replacing, experienced RF/Core network engineers. --- ## Citation ```bibtex @misc{telecom-intelligence-v7, author = {mindfossil}, title = {Telecom Intelligence Model v7 — Qwen2.5-7B Fine-Tuned for Network Operations}, year = {2026}, publisher = {HuggingFace}, url = {https://huggingface.co/mindfossil/telecom-intelligence-model-v7-merged} } ```