Text Generation
Transformers
Safetensors
English
code
code-generation
cli
bash
python
terminal
automation
lora
fine-tuned
conversational
Instructions to use Maarij-Aqeel/CLI_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Download adapter_model.safetensors from Maarij-Aqeel/CLI_model: direct link, hf CLI and curl.
- Browser
- Download file 771 MB
-
https://huggingface.co/Maarij-Aqeel/CLI_model/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://Maarij-Aqeel/CLI_model/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/Maarij-Aqeel/CLI_model/resolve/main/adapter_model.safetensors
771 MB
- Xet hash:
- 25a1735bc65090307813b43b1d305082ce0c2bea8d3c64f4a5335eb124557899
- Size of remote file:
- 771 MB
- SHA256:
- 0206143278c3960798d35c081136050aa6a319793777bb1c7549a2f15b2b8e22
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