Instructions to use NickH01/mistral-network-testplan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NickH01/mistral-network-testplan with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NickH01/mistral-network-testplan")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NickH01/mistral-network-testplan")
model = AutoModelForCausalLM.from_pretrained("NickH01/mistral-network-testplan")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NickH01/mistral-network-testplan with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NickH01/mistral-network-testplan"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NickH01/mistral-network-testplan",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NickH01/mistral-network-testplan

SGLang

How to use NickH01/mistral-network-testplan with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NickH01/mistral-network-testplan" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NickH01/mistral-network-testplan",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NickH01/mistral-network-testplan" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NickH01/mistral-network-testplan",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NickH01/mistral-network-testplan with Docker Model Runner:
```
docker model run hf.co/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.

Downloads last month: -