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---
license: apache-2.0
language: en
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation
- endpoints
- finetuned
- transformers
inference: true
---
# Mistral-7B-Instruct Network Test Plan Generator (LoRA Fine-Tuned)
This model is a fine-tuned version of [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/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](https://huggingface.co/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:
```python
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](https://mistral.ai/)
- Fine-tuning and evaluation powered by 🤗 Transformers, PEFT, and TRL
## 📫 Contact
For questions or collaboration, reach out to me via Hugging Face.
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