mindfossil/telecom-intelligence-model-v7-merged

Telecom Intelligence — Qwen2.5-7B v7

A domain-fine-tuned LLM for telecom network operations, built on Qwen/Qwen2.5-7B-Instruct using QLoRA (4-bit) + SFT via 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

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:

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)

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

@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}
}