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", 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
| # 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 openai import OpenAI | |
| from transformers.utils.versions import require_version | |
| require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0") | |
| def main(): | |
| client = OpenAI( | |
| api_key="{}".format(os.getenv("API_KEY", "0")), | |
| base_url="http://localhost:{}/v1".format(os.getenv("API_PORT", 8000)), | |
| ) | |
| messages = [] | |
| messages.append( | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "text", "text": "Output the color and number of each box."}, | |
| { | |
| "type": "image_url", | |
| "image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/boxes.png"}, | |
| }, | |
| ], | |
| } | |
| ) | |
| result = client.chat.completions.create(messages=messages, model="test") | |
| messages.append(result.choices[0].message) | |
| print("Round 1:", result.choices[0].message.content) | |
| # The image shows a pyramid of colored blocks with numbers on them. Here are the colors and numbers of ... | |
| messages.append( | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "text", "text": "What kind of flower is this?"}, | |
| { | |
| "type": "image_url", | |
| "image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/flowers.jpg"}, | |
| }, | |
| ], | |
| } | |
| ) | |
| result = client.chat.completions.create(messages=messages, model="test") | |
| messages.append(result.choices[0].message) | |
| print("Round 2:", result.choices[0].message.content) | |
| # The image shows a cluster of forget-me-not flowers. Forget-me-nots are small ... | |
| if __name__ == "__main__": | |
| main() | |