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mindfossil
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Model Details
Model Provider
mindfossil
Model Tree
unsloth/Qwen2.5-7B-Instruct-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
The model reasons step-by-step over telecom operational data to:
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 OLLApm*, Huawei L.* / VS.5G.*, Nokia RRC_CONN_*, ZTE LTE PM naming conventions; maps all to equivalent KPI formulasBLK/UBL, LST, DSP, MOD, RST, ACT, DEA, ADD, RMV; includes RST NRDUCELL (5G DU cell reset procedure)get/set/la/st commands; targets correct MO classes (EUtranCellFDD, NRCellDU, AntennaUnitGroup, RetSubUnit); uses administrativeState attributepmPdcpVolDlDrb + pmPrbUsedDlSum with correct 18 MHz used BW for 20 MHz LTE| 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) |
New in v7 (batches 47–52, ~110 examples):
pmRrcConnEstabFail by cause codeVS.5G.* PM counter familiespython
from transformers import AutoModelForCausalLM, AutoTokenizerimport 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) # Exampleprint(ask( "Ericsson cell ENB010-CELL01: pmRrcConnEstabAtt=948, pmRrcConnEstabSucc=921, " "pmErabEstabAttInit=921, pmErabEstabSuccInit=902, pmHoExeAttLteIntraF=421, " "pmHoExeSuccLteIntraF=412. Analyse for anomalies."))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"]bash
pip install vllmvllm serve mindfossil/telecom-intelligence-model-v7-merged \ --dtype float16 \ --max-model-len 4096 \ --gpu-memory-utilization 0.90PM counter anomaly detection:
text
TASK: Anomaly DetectionVENDOR: Ericsson | Cell: ENB042-CELL07 | Granularity: 15 minpmRrcConnEstabAtt=941, pmRrcConnEstabSucc=908pmErabEstabAttInit=941, pmErabEstabSuccInit=824pmHoExeAttLteIntraF=378, pmHoExeSuccLteIntraF=288Analyse for anomalies. Derive all KPIs showing your arithmetic, then state the verdict.5G NR beam failure diagnosis:
text
After an antenna upgrade at a 5G NR cell, UEs near the cell edge show intermittentdisconnections. 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:
text
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:
text
TASK: AMOS CLI command generationVENDOR: Ericsson | Node: GNBDU-SITE-05Generate AMOS commands to:1. Lock all NRCellDU cells (administrativeState)2. Retrieve pmNrRrcConnEstabSucc and pmNrRrcConnEstabAtt for all cells3. Unlock NRCellDU=Cell-1bibtex
@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}}pmErabEstabSuccInitMmeTrigger (MME-triggered E-RAB)pmHoPrepSuccLteIntraF + full HO KPI chainRST NRDUCELL (cell reset procedure + precautions)