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Model: mindfossil/telecom-intelligence-model-v2-merged Source: Original Platform
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180
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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---
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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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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
|
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{{- '}\n</tool_call>' }}
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{%- endfor %}
|
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{{- '<|im_end|>\n' }}
|
||||||
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{%- elif message.role == "tool" %}
|
||||||
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
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{{- '<|im_start|>user' }}
|
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{%- endif %}
|
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{{- '\n<tool_response>\n' }}
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||||||
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{{- message.content }}
|
||||||
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{{- '\n</tool_response>' }}
|
||||||
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- if add_generation_prompt %}
|
||||||
|
{{- '<|im_start|>assistant\n' }}
|
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|
{%- endif %}
|
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config.json
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config.json
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{
|
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"architectures": [
|
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|
"Qwen2ForCausalLM"
|
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|
],
|
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|
"attention_dropout": 0.0,
|
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|
"bos_token_id": null,
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"torch_dtype": "bfloat16",
|
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|
"eos_token_id": 151645,
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"hidden_act": "silu",
|
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|
"hidden_size": 3584,
|
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|
"initializer_range": 0.02,
|
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|
"intermediate_size": 18944,
|
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|
"layer_types": [
|
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|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
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|
"full_attention",
|
||||||
|
"full_attention",
|
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|
"full_attention",
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|
"full_attention",
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|
"full_attention",
|
||||||
|
"full_attention",
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||||||
|
"full_attention",
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|
"full_attention",
|
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|
"full_attention",
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|
"full_attention",
|
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|
"full_attention",
|
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|
"full_attention",
|
||||||
|
"full_attention",
|
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|
"full_attention",
|
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|
"full_attention",
|
||||||
|
"full_attention",
|
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|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention"
|
||||||
|
],
|
||||||
|
"max_position_embeddings": 32768,
|
||||||
|
"max_window_layers": 28,
|
||||||
|
"model_type": "qwen2",
|
||||||
|
"num_attention_heads": 28,
|
||||||
|
"num_hidden_layers": 28,
|
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|
"num_key_value_heads": 4,
|
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|
"pad_token_id": 151654,
|
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|
"rms_norm_eps": 1e-06,
|
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|
"rope_parameters": {
|
||||||
|
"rope_theta": 1000000.0,
|
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|
"rope_type": "default"
|
||||||
|
},
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": false,
|
||||||
|
"unsloth_fixed": true,
|
||||||
|
"unsloth_version": "2026.7.5",
|
||||||
|
"use_cache": true,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 152064
|
||||||
|
}
|
||||||
14
generation_config.json
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generation_config.json
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|||||||
|
{
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
151645,
|
||||||
|
151643
|
||||||
|
],
|
||||||
|
"max_length": 32768,
|
||||||
|
"pad_token_id": 151654,
|
||||||
|
"repetition_penalty": 1.05,
|
||||||
|
"temperature": 0.7,
|
||||||
|
"top_k": 20,
|
||||||
|
"top_p": 0.8,
|
||||||
|
"transformers_version": "5.5.0"
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|
||||||
|
"model.norm.weight": "model-00003-of-00004.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:6b4360dd6a184650ffc48056c2569bc603f896c5adfe94b10f1c79f809638aa5
|
||||||
|
size 11422166
|
||||||
194
tokenizer_config.json
Normal file
194
tokenizer_config.json
Normal file
@@ -0,0 +1,194 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"is_local": false,
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|vision_pad|>",
|
||||||
|
"padding_side": "left",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151646": {
|
||||||
|
"content": "<|object_ref_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151647": {
|
||||||
|
"content": "<|object_ref_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151648": {
|
||||||
|
"content": "<|box_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151649": {
|
||||||
|
"content": "<|box_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151650": {
|
||||||
|
"content": "<|quad_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151651": {
|
||||||
|
"content": "<|quad_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151652": {
|
||||||
|
"content": "<|vision_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151653": {
|
||||||
|
"content": "<|vision_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151654": {
|
||||||
|
"content": "<|vision_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151655": {
|
||||||
|
"content": "<|image_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151656": {
|
||||||
|
"content": "<|video_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151657": {
|
||||||
|
"content": "<tool_call>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151658": {
|
||||||
|
"content": "</tool_call>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151659": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151660": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151661": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user