Instructions to use Maarij-Aqeel/CLI_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Maarij-Aqeel/CLI_model with Transformers:

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

pipe = pipeline("text-generation", model="Maarij-Aqeel/CLI_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Maarij-Aqeel/CLI_model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Maarij-Aqeel/CLI_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Maarij-Aqeel/CLI_model

SGLang

How to use Maarij-Aqeel/CLI_model 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 "Maarij-Aqeel/CLI_model" \
    --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": "Maarij-Aqeel/CLI_model",
		"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 "Maarij-Aqeel/CLI_model" \
        --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": "Maarij-Aqeel/CLI_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Maarij-Aqeel/CLI_model with Docker Model Runner:
```
docker model run hf.co/Maarij-Aqeel/CLI_model
```

🛠️ cli-model-deepseek-v2-lite-finetuned

A fine-tuned version of DeepSeek-Coder-V2-Lite-Base specialized in generating and understanding command-line interface (CLI) tools, automation scripts, and terminal-based workflows.

🧠 Model Details

Field	Value
Base Model	DeepSeek-Coder-V2-Lite-Base
Fine-tuned on	Custom CLI-focused code dataset
Objective	Next-token prediction / code generation
Domain	CLI tools, Bash scripts, Python CLI apps
Framework	HuggingFace Transformers (PyTorch)
Finetuning Method	LoRA (Low-Rank Adaptation)
Hardware	2× Tesla T4 GPUs
Training Duration	~5 hours

📂 Training Data

The model was trained on a custom dataset containing real-world command-line and scripting examples:

Shell scripts (.sh)
Python CLI utilities
Terminal workflows (e.g., Git, curl)
API wrappers and automation scripts
Dockerfiles and Makefiles

Format: JSONL with {"prompt": ..., "completion": ...} pairs

🎯 Use Cases

This model is designed for developers who:

Want to generate CLI-based tools and scaffolds
Need help with automating tasks using Bash or Python
Build terminal wrappers around REST APIs
Create documentation and examples for terminal apps

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