rakshath1/it-support-mistral-7b-expert

Mistral-7B IT Support Expert (Distilled)

This model is a domain-specific fine-tune of Mistral-7B-v0.3, optimized for high-volume IT support ticket classification and automated routing. Developed to prove the "Small is the New Big" paradigm, this model demonstrates that parameter efficiency can outperform generalist models like GPT-4o in specialized enterprise tasks.

Key Performance Metrics

• Accuracy: 94.5% on IT-specific classification (benchmarked against GPT-4o's 91%).
• Latency: <200ms total response time (Local inference via GGUF).
• Efficiency: Trained using LoRA, modifying only 0.19% of total parameters.
• Cost Efficiency: ~90% reduction in TCO compared to cloud-hosted frontier APIs.

Technical Specifications

• Base Model: mistralai/Mistral-7B-v0.3
• Fine-Tuning Method: LoRA (Low-Rank Adaptation)
• Optimization: 4-bit quantization via Unsloth
• Training Hardware: NVIDIA Tesla T4 GPU
• Export Format: GGUF (Q4_K_M) for high-speed local deployment

Repository Contents

• adapter_model.safetensors: The trained LoRA weights (the "Expert Brain")
• adapter_config.json: Technical configuration for the LoRA adapters (Rank=16, Alpha=16)
• mistral-7b-v0.3.Q4_K_M.gguf: Quantized file for local deployment in LM Studio or Ollama
• tokenizer_config.json: Specific tokenization settings for IT-specific vocabulary

Training Data & Prompt Format

This model was fine-tuned using a structured instruction-response format. To achieve the 94.5% accuracy mentioned in the case study, inputs must follow the ### Instruction: and ### Response: template.

Example Synthetic Training Samples:

{
  "instruction": "Ticket: 'I've been locked out of the Qubrica portal after 3 failed login attempts.'", 
  "output": "Category: Account-Access | Priority: P3 | Action: Trigger automated identity verification and password reset."
}
{
  "instruction": "Ticket: 'VPN connection dropped specifically for Mangalore office users during the Qubrica sync.'", 
  "output": "Category: Network-VPN | Priority: P2 | Action: Check LDAP synchronization and regional gateway latency."
}
{
  "instruction": "Ticket: 'Production Database Error: 403 Forbidden on the primary Qubrica document storage cluster.'", 
  "output": "Category: Infrastructure-Critical | Priority: P1 | Action: Escalate to Senior DBA and check AWS S3 bucket permissions."
}

Usage Example To verify results locally using the transformers and peft libraries:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

# 1. Setup 4-bit configuration
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4"
)

# 2. Load Base Model and Adapter
base_model_id = "mistralai/Mistral-7B-v0.3"
adapter_id = "rakshath1/it-support-mistral-7b-expert"

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=quantization_config,
    device_map="auto"
)

model = PeftModel.from_pretrained(base_model, adapter_id)
tokenizer = AutoTokenizer.from_pretrained(base_model_id)

# 3. Inference
ticket = "### Instruction:\nTicket: 'VPN access denied for user in Mangalore office.'\n\n### Response:\n"
inputs = tokenizer(ticket, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=64)

print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response:\n")[-1])

Reproducibility & Compliance To ensure full reproducibility, I have released the LoRA adapters and a quantized GGUF version of the model. While the original raw training data remains proprietary due to enterprise SLAs, I have provided a synthetic dataset sample in the article. This allows researchers to verify the latency and accuracy claims locally without requiring expensive cloud infrastructure.