Text Generation
Transformers
Safetensors
Arabic
qwen
llama-factory
lora
arabic
question-answering
instruction-tuning
kaggle
fine-tuned
conversational
Instructions to use youssefedweqd/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use youssefedweqd/working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="youssefedweqd/working") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("youssefedweqd/working", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use youssefedweqd/working with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "youssefedweqd/working" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "youssefedweqd/working", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/youssefedweqd/working
- SGLang
How to use youssefedweqd/working 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 "youssefedweqd/working" \ --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": "youssefedweqd/working", "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 "youssefedweqd/working" \ --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": "youssefedweqd/working", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use youssefedweqd/working with Docker Model Runner:
docker model run hf.co/youssefedweqd/working
File size: 457 Bytes
cd63278 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | .PHONY: build commit license quality style test
check_dirs := scripts src tests setup.py
build:
pip3 install build && python3 -m build
commit:
pre-commit install
pre-commit run --all-files
license:
python3 tests/check_license.py $(check_dirs)
quality:
ruff check $(check_dirs)
ruff format --check $(check_dirs)
style:
ruff check $(check_dirs) --fix
ruff format $(check_dirs)
test:
CUDA_VISIBLE_DEVICES= WANDB_DISABLED=true pytest -vv tests/
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