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://<your-endpoint>.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}
}

mindfossil/telecom-intelligence-model-v7-GGUF

Telecom Intelligence — Qwen2.5-7B v7