NickH01/mistral-network-testplan

Mistral-7B-Instruct Network Test Plan Generator (LoRA Fine-Tuned)

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 using LoRA (Low-Rank Adaptation). It was trained specifically to generate detailed and structured network test plans based on prompts describing test scopes or network designs.

🧠 Model Purpose

This model helps network test engineers generate realistic, complete test plans for:

• Validating routing protocols (e.g., BGP, OSPF)
• Validating various network design on multi-vendor hardware (Palo Alto, F5, Cisco, Nokia, etc)
• Firewall zero-trust configuration, HA setups, traffic load balancing, etc.
• Performance, security, and negative test scenarios
• Use cases derived from actual enterprise-level TestRail test plans

📌 Example Prompt

Write a detailed network test plan for the F5 BIG-IP software regression version 17.1.1.1.

Include the following sections: Introduction, Objectives, Environment Setup, at least 6 distinct Test Cases (covering functional, negative, performance, failover/HA, and security scenarios), and a final Conclusion. Each test case should include: Test Pre-conditions, Test Steps, and Expected Results. Use real-world examples, KPIs (e.g., CPU < 70%, response time < 200ms), and mention pass/fail criteria.

✅ Example Output

The model generates well-structured outputs, such as:

• A comprehensive Introduction
• Clear Objectives
• Environment Setup with lab configurations
• Multiple Test Cases including pre-conditions, test steps, and expected results
• A summarizing Conclusion

🔧 Technical Details

• Base model: mistralai/Mistral-7B-Instruct-v0.2
• LoRA config:
  • r=64
  • lora_alpha=16
  • target_modules=["q_proj", "v_proj"]
  • lora_dropout=0.1
  • task_type="CAUSAL_LM"
• Quantization: 8-bit (BitsAndBytes)

🏁 Inference

You can run inference using the 🤗 transformers pipeline:

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

model_path = "your-username/mistral-network-testplan-generator"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", torch_dtype="auto")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a detailed network test plan for validating OSPF redistribution into BGP."
response = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.7)[0]["generated_text"]

print(response)

📁 Files Included

• adapter_config.json, adapter_model.bin — if using LoRA only
• Full merged model weights — if you're uploading the full merged model

🚧 Limitations

• Currently trained on internal TestRail-style data
• Fine-tuned only on English prompts
• May hallucinate topology details unless provided explicitly

🔐 Access

This model may require requesting access if hosted under a gated repo due to Mistral license restrictions.

🙌 Acknowledgments

• Base model by Mistral AI
• Fine-tuning and evaluation powered by 🤗 Transformers, PEFT, and TRL

📫 Contact

For questions or collaboration, reach out to me via Hugging Face.