Instructions to use YousefBarakat/Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use YousefBarakat/Model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "YousefBarakat/Model") - Transformers
How to use YousefBarakat/Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="YousefBarakat/Model") 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("YousefBarakat/Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use YousefBarakat/Model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "YousefBarakat/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": "YousefBarakat/Model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/YousefBarakat/Model
- SGLang
How to use YousefBarakat/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 "YousefBarakat/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": "YousefBarakat/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 "YousefBarakat/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": "YousefBarakat/Model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use YousefBarakat/Model with Docker Model Runner:
docker model run hf.co/YousefBarakat/Model
Training in progress, step 410
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- trainer_log.jsonl +3 -0
adapter_model.safetensors
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{"current_steps": 390, "total_steps": 410, "loss": 0.444692325592041, "lr": 7.970185363271431e-07, "epoch": 0.9523809523809523, "percentage": 95.12, "elapsed_time": "1:22:51", "remaining_time": "0:04:14"}
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{"current_steps": 400, "total_steps": 410, "loss": 0.5859388828277587, "lr": 2.1910634183644474e-07, "epoch": 0.9768009768009768, "percentage": 97.56, "elapsed_time": "1:24:46", "remaining_time": "0:02:07"}
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{"current_steps": 400, "total_steps": 410, "eval_loss": 0.3502028286457062, "epoch": 0.9768009768009768, "percentage": 97.56, "elapsed_time": "1:27:22", "remaining_time": "0:02:11"}
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{"current_steps": 390, "total_steps": 410, "loss": 0.444692325592041, "lr": 7.970185363271431e-07, "epoch": 0.9523809523809523, "percentage": 95.12, "elapsed_time": "1:22:51", "remaining_time": "0:04:14"}
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{"current_steps": 400, "total_steps": 410, "loss": 0.5859388828277587, "lr": 2.1910634183644474e-07, "epoch": 0.9768009768009768, "percentage": 97.56, "elapsed_time": "1:24:46", "remaining_time": "0:02:07"}
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{"current_steps": 400, "total_steps": 410, "eval_loss": 0.3502028286457062, "epoch": 0.9768009768009768, "percentage": 97.56, "elapsed_time": "1:27:22", "remaining_time": "0:02:11"}
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{"current_steps": 410, "total_steps": 410, "loss": 0.6934732913970947, "lr": 1.8121093383671738e-09, "epoch": 1.0, "percentage": 100.0, "elapsed_time": "1:29:17", "remaining_time": "0:00:00"}
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{"current_steps": 410, "total_steps": 410, "epoch": 1.0, "percentage": 100.0, "elapsed_time": "1:31:56", "remaining_time": "0:00:00"}
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