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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("youssefedweqd/working", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
| # Copyright 2025 the LlamaFactory team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import os | |
| from dataclasses import dataclass, field | |
| from typing import Literal, Optional | |
| from datasets import DownloadMode | |
| class EvaluationArguments: | |
| r"""Arguments pertaining to specify the evaluation parameters.""" | |
| task: str = field( | |
| metadata={"help": "Name of the evaluation task."}, | |
| ) | |
| task_dir: str = field( | |
| default="evaluation", | |
| metadata={"help": "Path to the folder containing the evaluation datasets."}, | |
| ) | |
| batch_size: int = field( | |
| default=4, | |
| metadata={"help": "The batch size per GPU for evaluation."}, | |
| ) | |
| seed: int = field( | |
| default=42, | |
| metadata={"help": "Random seed to be used with data loaders."}, | |
| ) | |
| lang: Literal["en", "zh"] = field( | |
| default="en", | |
| metadata={"help": "Language used at evaluation."}, | |
| ) | |
| n_shot: int = field( | |
| default=5, | |
| metadata={"help": "Number of examplars for few-shot learning."}, | |
| ) | |
| save_dir: Optional[str] = field( | |
| default=None, | |
| metadata={"help": "Path to save the evaluation results."}, | |
| ) | |
| download_mode: DownloadMode = field( | |
| default=DownloadMode.REUSE_DATASET_IF_EXISTS, | |
| metadata={"help": "Download mode used for the evaluation datasets."}, | |
| ) | |
| def __post_init__(self): | |
| if self.save_dir is not None and os.path.exists(self.save_dir): | |
| raise ValueError("`save_dir` already exists, use another one.") | |