108 lines
2.7 KiB
Markdown
108 lines
2.7 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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base_model: mdk615661/it-helpdesk-merged-v3
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pipeline_tag: text-generation
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tags:
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- helpdesk
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- classification
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- it-support
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- fine-tuned
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- lora
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- mistral
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datasets:
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- mdkaif12/it-helpdesk-v4
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---
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# IT Helpdesk AI Classifier — v4
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Fine-tuned LLM for classifying IT helpdesk tickets into categories, subcategories, and generating insights for corporate IT support teams.
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## Model Details
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- **Base Model:** mdk615661/it-helpdesk-merged-v3
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- **Fine-tuning:** QLoRA (LoRA r=16, alpha=32)
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- **Training Data:** 2,000 IT helpdesk records (Qwen-generated)
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- **Training Loss:** 0.187 (3 epochs)
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- **Hardware:** Kaggle T4 GPU (33 min)
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- **Precision:** fp16
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "mdk615661/it-helpdesk-merged-v4"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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dtype=torch.float16,
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device_map="auto"
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)
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prompt = """### Instruction:
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Normalize and classify this IT helpdesk ticket.
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### Input:
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Laptop is not turning on
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### Output:
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(
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**inputs,
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max_new_tokens=150,
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do_sample=False,
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repetition_penalty=1.3,
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pad_token_id=tokenizer.eos_token_id
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)
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print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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## Output Format
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Category: Hardware
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SubCategory: Hardware - Laptop
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Normalized: laptop not working
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Priority: Medium
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Insight: Hardware failure preventing user from working.
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Recommendation: Raise repair request with IT hardware team.
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## Categories
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| Category | Subcategories |
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|---|---|
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| Hardware | Laptop, Charger, Mobile |
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| Software | Installation, VPN, Password Reset, O365, Teams Issue, MFA Reset, BitLocker, OS Installation, USB Access |
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| Incident | Critical Incident, Network Outage, Security Incident, Service Outage, Performance Incident |
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| Procurement | Hardware Procurement, Software Procurement |
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| Onboarding & Offboarding | Onboarding, Offboarding |
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| Cloud & Infrastructure | DR and BCP, Infrastructure Configuration, Network Configuration Request, System Configuration |
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| Asset | Asset Management, Asset Request, Asset - Client |
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| Others | Account Management, Audit and Compliance, Change Management, IT Training Request, Vendor Support |
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## Training Hyperparameters
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| Parameter | Value |
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|---|---|
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| Epochs | 3 |
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| Batch size | 4 |
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| Gradient accumulation | 4 |
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| Learning rate | 2e-4 |
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| Warmup steps | 50 |
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| LR scheduler | cosine |
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| LoRA r | 16 |
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| LoRA alpha | 32 |
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## Version History
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| Version | Training Data | Final Loss |
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|---|---|---|
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| v3 | 1,141 real TruMIS tickets | — |
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| **v4 (this)** | + 2,000 Qwen records | **0.187** |
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