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Model: mindfossil/telecom-intelligence-model-v2-merged Source: Original Platform
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README.md
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README.md
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# Telco_Intelligence v2
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A 7B parameter language model fine-tuned for telecom network operations, covering RAN anomaly detection, 5G Core root cause analysis, multi-vendor KPI normalisation, and CLI command generation across Ericsson, Nokia, and Huawei.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| **Base model** | Qwen2.5-7B |
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| **Fine-tuning method** | Supervised Fine-Tuning (SFT) with Chain-of-Thought |
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| **Model ID** | `mindfossil/telecom-intelligence-model-v2-merged` |
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| **Serving** | vLLM OpenAI-compatible endpoint |
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| **License** | Apache 2.0 |
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---
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## Capabilities
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- **PM KPI derivation** — computes KPIs from raw PM counter values with explicit step-by-step arithmetic: RRC Success Rate, ERAB SR, Handover SR, PRB Utilisation, Throughput, and vendor-equivalent formulas across Ericsson, Nokia, and Huawei naming conventions
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- **RAN anomaly detection** — applies derived KPIs against operational thresholds to classify cell health, identify degraded KPIs, and recommend remediation actions
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- **5G Core RCA** — analyses SMF/AMF telemetry (ContextDao latency, PM time series) and classifies faults (CPU fault, IP pool exhaustion, insufficient data)
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- **Core network probe / DPI analysis** — interprets passive probe data, xDR records, and interface-level flow telemetry to diagnose subscriber experience and network-side faults
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- **NWDAF-style analytics** — processes aggregated network data to identify patterns, predict load, and surface anomalies at the network function level
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- **Differential diagnosis** — distinguishes RAN-side vs Core-side faults using cross-KPI reasoning
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- **Multi-vendor KPI normalisation** — maps vendor-specific counter names to unified KPI formulas and computes comparable values across vendors
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- **SON energy saving** — applies policy gate conditions to produce explicit switch-off decisions
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- **NL → CLI** — translates natural language operations requests into valid Ericsson MML and AMOS CLI commands
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---
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## Evaluation
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Evaluated using deterministic scoring (numeric formula accuracy ±3%, keyword matching, reasoning chain validation, and hallucination guards) across 20 functional test cases.
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| Domain | Score | % |
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|---|---|---|
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| RAN anomaly detection | 110/136 | 81% |
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| 5G Core RCA | 97/128 | 76% |
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| SON policy | 27/44 | 61% |
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| NL → CLI | 55/82 | 67% |
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| Multi-vendor | 68/76 | 89% |
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| **Overall** | **357/466** | **77%** |
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Separately evaluated on a structured 5GC telemetry RCA benchmark (7 test cases): **12/14 (86%)**, above the 86% deployment confidence threshold.
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---
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## Quickstart
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "mindfossil/telecom-intelligence-model-v2-merged"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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system_prompt = (
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"You are a telecom network intelligence assistant specialising in RAN performance "
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"analysis, 5G Core operations, and multi-vendor troubleshooting. When given raw PM "
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"counters or telemetry data, derive KPIs showing your arithmetic step-by-step, "
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"compare against known thresholds, identify anomalies, and recommend actions."
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)
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prompt = (
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"TASK: Anomaly Detection\n"
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"DATA TYPE: RAN PM counters (4G LTE)\n"
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"VENDOR: Ericsson | Cell: ENB010-CELL01 | Granularity: 15 min\n\n"
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"RAW PM COUNTERS:\n"
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" pmRrcConnEstabSucc: 921\n"
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" pmRrcConnEstabAtt: 948\n"
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" pmErabEstabSuccInit: 902\n"
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" pmErabEstabAttInit: 921\n"
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" pmHoExecSuccLteIntraF: 412\n"
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" pmHoExecAttLteIntraF: 421\n\n"
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"Analyse these counters. Derive the relevant KPIs showing your arithmetic, "
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"then state the cell health verdict."
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)
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt},
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]
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(**model_inputs, max_new_tokens=1500, temperature=0.1)
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generated_ids = [
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output_ids[len(input_ids):]
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for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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### Serving with vLLM
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```bash
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vllm serve mindfossil/telecom-intelligence-model-v2-merged \
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--tensor-parallel-size 1 \
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--max-model-len 4096 \
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--gpu-memory-utilization 0.85
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```
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The model runs on a single **NVIDIA L4 (24 GB)** or equivalent GPU.
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---
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## Input / Output Format
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### RAN PM counter analysis
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**Input:**
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```
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TASK: Anomaly Detection
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DATA TYPE: RAN PM counters (4G LTE)
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VENDOR: Ericsson | Cell: <cell_id> | Granularity: 15 min
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RAW PM COUNTERS:
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pmRrcConnEstabSucc: <value>
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pmRrcConnEstabAtt: <value>
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...
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Analyse these counters for anomalies. Derive the relevant KPIs showing your
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arithmetic, then state the cell health verdict.
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```
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**Output:**
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```
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Step 1: Identify Vendor and Counter Naming Convention
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...
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Step 2: Derive Relevant KPIs
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RRC SR = pmRrcConnEstabSucc / pmRrcConnEstabAtt × 100 = 921 / 948 × 100 = 97.2%
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...
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Verdict: Healthy — all KPIs above threshold.
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```
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### 5GC telemetry RCA
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**Input:**
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```
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ContextDao Actor Latency (10-second window):
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Tag | Avg(ms) | Min(ms) | Max(ms) | 95th(ms)
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put-NrSessionContext | 11.40 | 4.20 | 55.00 | 97.00
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...
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```
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**Output:**
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```
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[ANOMALY DETECTION] — An anomaly is detected in the put-NrSessionContext operation.
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[REASONING] — ...
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Anomaly_Class: SMF_CPU_FAULT
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[ROOT CAUSE] — CPU starvation causing elevated ContextDao write latency.
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[REMEDIATION] — Scale SMF resources; investigate CPU spike root cause.
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```
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---
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## Citation
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```bibtex
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@misc{telecom-intelligence-v2-2026,
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title = {Telco\_Intelligence v2: A Telecom Operations Fine-Tuned Language Model},
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author = {mindfossil},
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year = {2026},
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url = {https://huggingface.co/mindfossil/telecom-intelligence-model-v2-merged}
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}
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```
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