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
Download Model/LLaMA-Factory/wandb/debug-internal.log from youssefedweqd/working: direct link, hf CLI and curl.
- Browser
- Download file 811 Bytes
-
https://huggingface.co/youssefedweqd/working/resolve/main/Model/LLaMA-Factory/wandb/debug-internal.log
- Command line
-
hf download hf://youssefedweqd/working/Model/LLaMA-Factory/wandb/debug-internal.log
-
curl -L -o debug-internal.log https://huggingface.co/youssefedweqd/working/resolve/main/Model/LLaMA-Factory/wandb/debug-internal.log
811 Bytes
| {"time":"2025-06-18T02:04:45.169771821Z","level":"INFO","msg":"stream: starting","core version":"0.19.9","symlink path":"/kaggle/working/LLaMA-Factory/wandb/run-20250618_020445-o5waoqcx/logs/debug-core.log"} | |
| {"time":"2025-06-18T02:04:45.427972883Z","level":"INFO","msg":"created new stream","id":"o5waoqcx"} | |
| {"time":"2025-06-18T02:04:45.428081239Z","level":"INFO","msg":"stream: started","id":"o5waoqcx"} | |
| {"time":"2025-06-18T02:04:45.428182138Z","level":"INFO","msg":"sender: started","stream_id":"o5waoqcx"} | |
| {"time":"2025-06-18T02:04:45.428270431Z","level":"INFO","msg":"handler: started","stream_id":"o5waoqcx"} | |
| {"time":"2025-06-18T02:04:45.428273846Z","level":"INFO","msg":"writer: Do: started","stream_id":"o5waoqcx"} | |
| {"time":"2025-06-18T02:04:45.721935563Z","level":"INFO","msg":"Starting system monitor"} | |