mindfossil/telecom-intelligence-model-v6-merged

import torch

SYSTEM_GENERAL = (
    "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 reports. You reason step by step using domain knowledge before producing conclusions."
)

SYSTEM_PRB = (
    "You are a telecom network intelligence assistant specialising in RAN capacity analysis. "
    "LTE PRB counts by bandwidth (3GPP TS 36.101): 1.4 MHz=6, 3 MHz=15, 5 MHz=25, "
    "10 MHz=50, 15 MHz=75, 20 MHz=100. "
    "For 5G NR 20 MHz with 15 kHz SCS: 106 PRBs. For 5G NR 100 MHz with 30 kHz SCS: 132 PRBs. "
    "The DL PRB utilisation formula is: PRB_util% = pmPrbUsedDlSum / (pmPrbUsedDlSamp x totalPRBs) x 100. "
    "Always state totalPRBs from the bandwidth before calculating. "
    "For 20 MHz LTE, totalPRBs is 100 — not 96, not 66, not 110."
)

SYSTEM_MML = (
    "You are a Huawei MML expert. Use ONLY these canonical Huawei MML command verbs: "
    "BLK (block/lock a cell or board), UBL (unblock/unlock a cell or board), "
    "LST (list configuration from database), DSP (display real-time operational state), "
    "MOD (modify a parameter value), RST (restart a board or process), "
    "ACT (activate a feature), DEA (deactivate a feature), ADD (add an object), RMV (remove an object). "
    "The following are NOT valid Huawei MML verbs and must never be used: "
    "BLOCK, UNBLOCK, SET, SHOW, DISPLAY, LIST, LOCK, UNLOCK, REBOOT, RESET."
)

SYSTEM_SON = (
    "You are a telecom network intelligence assistant specialising in SON Energy Saving. "
    "When evaluating ES cell switch-off, check these five conditions: "
    "(1) cell PRB utilisation < 30%, (2) active UE count < 10, "
    "(3) neighbour overlap > 80%, (4) neighbour PRB utilisation < 70%, "
    "(5) NOC approval = granted. "
    "If ALL five conditions pass, your first word must be ACTIVATE. "
    "If ANY condition fails, your first words must be DO NOT ACTIVATE, followed by the failing condition."
)

def _classify(question: str) -> str:
    q = question.lower()
    prb_keywords = ["prbuseddl", "prb util", "prb utiliz", "pmprb", "totalprbs",
                    "dl prb", "ul prb", "mhz lte", "mhz nr", "bandwidth", "prb sum", "prb samp"]
    mml_keywords = ["mml", "blk", "ubl", "lst ", "dsp ", "mod ", "rst ", "huawei command",
                    "block cell", "unblock cell", "lock cell", "unlock cell",
                    "localcellid", "nrcellid", "brd:", "cell:", "enodebfunction"]
    son_keywords = ["energy sav", "es activation", "activate energy", "son es",
                    "switch-off", "switch off", "cell sleep", "noc approv",
                    "neighbor prb", "neighbour prb", "neighbor overlap", "neighbour overlap"]
    if any(k in q for k in prb_keywords): return "prb"
    if any(k in q for k in mml_keywords): return "mml"
    if any(k in q for k in son_keywords): return "son"
    return "general"

def ask(question: str, system: str = None) -> str:
    if system is None:
        category = _classify(question)
        system_map = {"prb": SYSTEM_PRB, "mml": SYSTEM_MML, "son": SYSTEM_SON, "general": SYSTEM_GENERAL}
        system = system_map[category]
    messages = [{"role": "system", "content": system}, {"role": "user", "content": question}]
    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(text, return_tensors="pt").to(model.device)
    with torch.no_grad():
        out = model.generate(**inputs, max_new_tokens=1536, temperature=0.1,
            do_sample=True, repetition_penalty=1.1)
    return tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="mindfossil/telecom-intelligence-model-v6-merged",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "mindfossil/telecom-intelligence-model-v6-merged"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="eager"
)

Cell: pmPrbUsedDlSum=57120, pmPrbUsedDlSamp=68, 20 MHz LTE. What is the DL PRB utilisation %?
→ 84% (formula: 57120 / (68 × 100) × 100; totalPRBs=100 for 20 MHz LTE)

SON ES: cell PRB=19%, UEs=3, neighbour overlap=95%, neighbour PRB=52%, NOC approved. Activate?
→ ACTIVATE — all five thresholds pass

Block cell LocalCellId=3 on Huawei eNodeB for maintenance, then restore.
→ BLK CELL: LocalCellId=3;  /  UBL CELL: LocalCellId=3;

PDU session fails. AMF→SMF N11 healthy. SMF→UPF PFCP association times out. Which NF?
→ UPF is failing. Interface: N4 (SMF↔UPF). Check UPF process state and N4 connectivity.

RRC Setup SR formula for Ericsson, Huawei, Nokia?
→ pmRrcConnEstabSucc/Att × 100  |  L.RRC.ConnEstabSucc/Att × 100  |  RRC_CONN_SETUP_SUCC_SUM/ATT_SUM × 100

S1 Setup SCTP up but no response — what to check?
→ S1 is eNB↔MME (LTE/EPC, port 36412). Check PLMN/TAC match, eNB IP whitelist on MME, S1AP cause code.

VoLTE call drops 8s after 200 OK. TAS→UDR P99 = 4.2s. Root cause?
→ TAS session refresh timer expires waiting for UDR. Fix TAS→UDR latency below 500ms.

NTN LEO: MSG3 success 98%, MSG5 failure 89%. Why?
→ Timing advance drift between MSG2 RAR and MSG5 transmission. Set ra-ContentionResolutionTimer ≥ 64ms, enable NTN TA pre-compensation.