| TELECOM QLoRA FOUR-OUTPUT COMPLETE WORKSPACE |
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| Project structure |
| ================= |
| train.py |
| test.py |
| requirements.txt |
| run_all.sh |
| run_all.bat |
| README.txt |
|
|
| sample/ |
| data/ |
| train_four_output.jsonl |
| validation_four_output.jsonl |
| test_four_output.jsonl |
| .cache/ |
| huggingface/ |
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| models/ |
| (training output will be written here) |
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| Training |
| ======== |
| 1. Install dependencies: |
| pip install -r requirements.txt |
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| 2. Make sure CUDA + a compatible NVIDIA GPU/bitsandbytes setup is available. |
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| 3. Train: |
| python train.py |
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| 4. Evaluate: |
| python test.py --adapter "models/telecom-qwen-four-output" --test "sample/data/test_four_output.jsonl" --samples 0 |
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| Debug: |
| ====== |
| python test.py --adapter "models/telecom-qwen-four-output" --test "sample/data/test_four_output.jsonl" --samples 10 --debug |
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| Expected response sections |
| =========================== |
| Probable Root Cause: |
| Explanation: |
| Recommendation: |
| Fault Severity: |
| fault_severity=0|1|2 |
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| Important |
| ========= |
| Root Cause, Explanation, and Recommendation targets are structured, |
| data-constrained synthetic targets because the source dataset contains |
| fault_severity labels but not engineer-validated root-cause/remediation |
| annotations. See README.txt. |
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|
