Instructions to use mindfossil/telecom-intelligence-model-v6-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mindfossil/telecom-intelligence-model-v6-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mindfossil/telecom-intelligence-model-v6-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mindfossil/telecom-intelligence-model-v6-merged")
model = AutoModelForCausalLM.from_pretrained("mindfossil/telecom-intelligence-model-v6-merged", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mindfossil/telecom-intelligence-model-v6-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mindfossil/telecom-intelligence-model-v6-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mindfossil/telecom-intelligence-model-v6-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mindfossil/telecom-intelligence-model-v6-merged

SGLang

How to use mindfossil/telecom-intelligence-model-v6-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mindfossil/telecom-intelligence-model-v6-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mindfossil/telecom-intelligence-model-v6-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mindfossil/telecom-intelligence-model-v6-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mindfossil/telecom-intelligence-model-v6-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mindfossil/telecom-intelligence-model-v6-merged with Docker Model Runner:
```
docker model run hf.co/mindfossil/telecom-intelligence-model-v6-merged
```

Telecom Intelligence — Qwen2.5-7B v6

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.

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, and Nokia RAN/Core nodes
PRB utilisation — compute DL/UL PRB utilisation % correctly using pmPrbUsedDlSum / (pmPrbUsedDlSamp × totalPRBs) × 100 for all LTE bandwidths (6/15/25/50/75/100 PRBs) and 5G NR
SON Energy Saving decisions — evaluate ES cell switch-off against PRB, UE count, neighbour overlap, neighbour PRB, and NOC approval thresholds; always produces a binary ACTIVATE / DO NOT ACTIVATE decision
Multi-vendor counter normalisation — maps Ericsson (pmRrcConnEstabSucc / pmRrcConnEstabAtt × 100), Huawei (L.RRC.ConnEstabSucc / L.RRC.ConnEstabAtt × 100), and Nokia (RRC_CONN_SETUP_SUCC_SUM / RRC_CONN_SETUP_ATT_SUM × 100) counter names to equivalent KPI formulas
Huawei MML — outputs canonical Huawei MML commands using correct verbs: BLK/UBL (not BLOCK/UNBLOCK), MOD (not SET), LST, DSP, RST, ACT, DEA
5G Core NF attribution — names the exact failing Network Function (AMF, SMF, UPF, PCF, AUSF, UDM, NRF) and interface (N1, N2, N3, N4, N8, N11, etc.) rather than giving vague "core network" answers
S1/EPC fault chains — diagnoses S1 Setup failures (eNB↔MME, S1AP over SCTP port 36412), SGW path failures, MME overload cascades; correctly distinguished from 5G N2/NGAP (gNB↔AMF)
LTE vs 5G generation boundary — correctly identifies S1=LTE/EPC (eNB↔MME) vs N2=5G SA (gNB↔AMF) and never conflates them
IMS / VoNR — TAS→UDR latency diagnosis, correct SIP response codes (100/180/183/200/401/486/487/503/504), call drop RCA via session timer and UDR query timeout
O-RAN — fronthaul synchronisation failures (IEEE 1588v2 PTP), rApp/xApp policy collisions, Near-RT RIC / Non-RT RIC control loop conflicts
5GC security — GTP-U tunnel injection attacks, SEPP N32 JSON Patch integrity failures, uRPF bypass
NTN LEO satellite — 3GPP Rel-17 NTN HARQ feedback disable, timing advance pre-compensation, MSG3/MSG5 asymmetry diagnosis, Keplerian TA drift correction
Cloud-native NF — SR-IOV NUMA misalignment, DPDK RSS queue imbalance, Kubernetes pod CPU pinning faults
PromQL — writes energy efficiency and RAN KPI alert rules for Prometheus/O-RAN SMO

Smart query routing (recommended)

The model performs best when given a domain-specific system prompt. Use the ask() wrapper below — it automatically classifies the query and applies the right system prompt:

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)

Loading the model

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)

Or with standard Transformers (slower, no Unsloth optimisation):

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"
)

Example queries

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.

Eval results (v6, 20-question domain eval)

Domain	Score	Notes
PRB utilisation	2/2	totalPRBs correct for all bandwidths
Huawei MML	3/3	BLK/UBL/DSP/RST/MOD/LST all canonical
SON ES decisions	3/3	Binary ACTIVATE/DO NOT ACTIVATE, correct failing condition
5GC NF attribution	2/2	PFCP/UPF, N8/UDM correctly identified
EPC/LTE RCA	2/2	MME overload, S1 setup (eNB↔MME) correct
Multi-vendor RRC	1/1	×100 multiplier correct, all three vendors
IMS/VoNR	1/1	SIP codes correct, TAS→UDR root cause
O-RAN / NTN / Cloud NF	3/3	PTP sync, NTN HARQ, NUMA analysis
E-RAB formula	1/1	Counter structure and thresholds correct
Total	18/20	AMOS CLI not in scope (skipped)

Training

Parameter	Value
Base model	Qwen/Qwen2.5-7B-Instruct
Method	QLoRA (4-bit NF4) + SFT via Unsloth + TRL SFTTrainer
LoRA rank	16
LoRA alpha	32
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training examples	694
Epochs	3
Max sequence length	2048
Prompt format	ChatML (`<

Training data coverage (694 examples across 45 batches):

Ericsson LTE PM counter RCA (pmRrcConnEstab*, pmErab*, pmHo*, pmPrb*)
Ericsson 5G NR PM counters (pmNr* series)
Huawei LTE L.* counter RCA
Huawei 5G NR VS.* counter RCA
Huawei MML canonical command syntax (BLK/UBL/LST/DSP/MOD/RST/ACT/DEA)
Nokia NetAct CLI and M8xxx counter RCA
Multi-vendor KPI normalisation (RRC SSR, E-RAB SSR, HO SSR) — all ×100
CM configuration mismatch RCA
5G Core: AMF, SMF, UPF, PCF, AUSF, UDM, NRF fault diagnosis
Network slicing and NWDAF anomaly detection
Probe/xDR passive monitoring
S1/EPC differential diagnosis (eNB↔MME, port 36412, S1AP) vs N2/5G (gNB↔AMF, NGAP)
SON Energy Saving binary decision evaluation
PRB utilisation formula (all LTE bandwidths + 5G NR)
IMS/VoNR: SIP call flows (100/180/183/200/401/486/487/503/504), TAS→UDR latency RCA
O-RAN WG4 Option 7.2x fronthaul synchronisation (PTP/IEEE 1588v2)
O-RAN RIC rApp/xApp policy collision resolution
3GPP Rel-17 NTN LEO satellite: HARQ feedback disable, TA pre-compensation, MSG3/MSG5 asymmetry
3GPP Rel-18 AI/ML RAN CSI model drift detection
5GC security: GTP-U injection, SEPP N32, uRPF
Cloud-native NF: SR-IOV, DPDK, NUMA, Kubernetes CPU pinning
PromQL for Green RAN energy efficiency metrics

Limitations

Trained on synthetic expert-authored examples, not live operator data exports
Counter names follow standard 3GPP/vendor documentation; site-specific customisations may differ
Max context 2048 tokens; very long counter dumps may need chunking
AMOS CLI (Ericsson MO-path syntax) is not a strength of this version — use vendor tooling for AMOS
Not a replacement for vendor tools (ENM, NetAct, U2000) — use for analysis assistance and NL→CLI translation
May occasionally output Chinese characters (Qwen base model bleed-through); add "Always respond in English only." to the system prompt if needed

Version history

Version	Examples	Notes
v1	~426	Initial — Ericsson LTE, Huawei L.*, multi-vendor normalisation
v2	511	Added 5GC, NWDAF, probes, Nokia, CM mismatch, 5G NR counters
v3	606	PRB formula, SON binary decisions, AMOS wildcards, 5GC NF attribution, MML canonicalisation
v4	635	PRB totalPRBs fix, SON logic, MML BLK/UBL, O-RAN, NTN, AI/ML RAN, 5GC security, IMS, cloud-native NF
v5	680	Heavy reinforcement on PRB/SON/MML; introduced smart system-prompt routing
v6	694	RRC ×100 multiplier fix, S1 vs N2 generation boundary, IMS SIP codes, NTN TA drift; eval score 18/20

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for mindfossil/telecom-intelligence-model-v6-merged

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(2935)

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