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--- |
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license: apache-2.0 |
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language: en |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation |
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- endpoints |
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- finetuned |
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- transformers |
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inference: true |
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--- |
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# Mistral-7B-Instruct Network Test Plan Generator (LoRA Fine-Tuned) |
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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. |
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## 🧠 Model Purpose |
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This model helps network test engineers generate realistic, complete test plans for: |
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- Validating routing protocols (e.g., BGP, OSPF) |
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- Validating various network design on multi-vendor hardware (Palo Alto, F5, Cisco, Nokia, etc) |
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- Firewall zero-trust configuration, HA setups, traffic load balancing, etc. |
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- Performance, security, and negative test scenarios |
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- Use cases derived from actual enterprise-level TestRail test plans |
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## 📌 Example Prompt |
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``` |
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Write a detailed network test plan for the F5 BIG-IP software regression version 17.1.1.1. |
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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. |
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``` |
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## ✅ Example Output |
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The model generates well-structured outputs, such as: |
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- A comprehensive **Introduction** |
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- Clear **Objectives** |
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- **Environment Setup** with lab configurations |
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- Multiple **Test Cases** including pre-conditions, test steps, and expected results |
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- A summarizing **Conclusion** |
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## 🔧 Technical Details |
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- **Base model**: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) |
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- **LoRA config**: |
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- `r=64` |
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- `lora_alpha=16` |
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- `target_modules=["q_proj", "v_proj"]` |
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- `lora_dropout=0.1` |
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- `task_type="CAUSAL_LM"` |
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- **Quantization**: 8-bit (BitsAndBytes) |
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## 🏁 Inference |
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You can run inference using the 🤗 `transformers` pipeline: |
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```python |
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM |
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model_path = "your-username/mistral-network-testplan-generator" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", torch_dtype="auto") |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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prompt = "Write a detailed network test plan for validating OSPF redistribution into BGP." |
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response = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.7)[0]["generated_text"] |
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print(response) |
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``` |
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## 📁 Files Included |
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- `adapter_config.json`, `adapter_model.bin` — if using LoRA only |
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- Full merged model weights — if you're uploading the full merged model |
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## 🚧 Limitations |
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- Currently trained on internal TestRail-style data |
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- Fine-tuned only on English prompts |
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- May hallucinate topology details unless provided explicitly |
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## 🔐 Access |
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This model may require requesting access if hosted under a gated repo due to Mistral license restrictions. |
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## 🙌 Acknowledgments |
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- Base model by [Mistral AI](https://mistral.ai/) |
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- Fine-tuning and evaluation powered by 🤗 Transformers, PEFT, and TRL |
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## 📫 Contact |
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For questions or collaboration, reach out to me via Hugging Face. |
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