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 LLaMA-Factory/evaluation/mmlu/mapping.json from youssefedweqd/working: direct link, hf CLI and curl.
- Browser
- Download file 5.16 kB
-
https://huggingface.co/youssefedweqd/working/resolve/main/LLaMA-Factory/evaluation/mmlu/mapping.json
- Command line
-
hf download hf://youssefedweqd/working/LLaMA-Factory/evaluation/mmlu/mapping.json
-
curl -L -o mapping.json https://huggingface.co/youssefedweqd/working/resolve/main/LLaMA-Factory/evaluation/mmlu/mapping.json
5.16 kB
| { | |
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| "name": "abstract algebra", | |
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| "name": "high school macroeconomics", | |
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| "name": "high school mathematics", | |
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| "name": "high school microeconomics", | |
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| "name": "high school physics", | |
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| "name": "high school psychology", | |
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| "high_school_statistics": { | |
| "name": "high school statistics", | |
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| "high_school_us_history": { | |
| "name": "high school us history", | |
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| "high_school_world_history": { | |
| "name": "high school world history", | |
| "category": "Humanities" | |
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| "human_aging": { | |
| "name": "human aging", | |
| "category": "Other" | |
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| "human_sexuality": { | |
| "name": "human sexuality", | |
| "category": "Social Sciences" | |
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| "international_law": { | |
| "name": "international law", | |
| "category": "Humanities" | |
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| "jurisprudence": { | |
| "name": "jurisprudence", | |
| "category": "Humanities" | |
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| "logical_fallacies": { | |
| "name": "logical fallacies", | |
| "category": "Humanities" | |
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| "machine_learning": { | |
| "name": "machine learning", | |
| "category": "STEM" | |
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| "management": { | |
| "name": "management", | |
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| "marketing": { | |
| "name": "marketing", | |
| "category": "Other" | |
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| "medical_genetics": { | |
| "name": "medical genetics", | |
| "category": "Other" | |
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| "miscellaneous": { | |
| "name": "miscellaneous", | |
| "category": "Other" | |
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| "moral_disputes": { | |
| "name": "moral disputes", | |
| "category": "Humanities" | |
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| "moral_scenarios": { | |
| "name": "moral scenarios", | |
| "category": "Humanities" | |
| }, | |
| "nutrition": { | |
| "name": "nutrition", | |
| "category": "Other" | |
| }, | |
| "philosophy": { | |
| "name": "philosophy", | |
| "category": "Humanities" | |
| }, | |
| "prehistory": { | |
| "name": "prehistory", | |
| "category": "Humanities" | |
| }, | |
| "professional_accounting": { | |
| "name": "professional accounting", | |
| "category": "Other" | |
| }, | |
| "professional_law": { | |
| "name": "professional law", | |
| "category": "Humanities" | |
| }, | |
| "professional_medicine": { | |
| "name": "professional medicine", | |
| "category": "Other" | |
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| "professional_psychology": { | |
| "name": "professional psychology", | |
| "category": "Social Sciences" | |
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| "public_relations": { | |
| "name": "public relations", | |
| "category": "Social Sciences" | |
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| "name": "security studies", | |
| "category": "Social Sciences" | |
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| "name": "sociology", | |
| "category": "Social Sciences" | |
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| "name": "us foreign policy", | |
| "category": "Social Sciences" | |
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| "name": "virology", | |
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| "name": "world religions", | |
| "category": "Humanities" | |
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| } | |