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README.md

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+# 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)
+- Testing firewall rules, HA setups, F5 BIG-IP software, 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: [your-email@example.com]