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 time | |
| from enum import Enum, unique | |
| from typing import Any, Optional, Union | |
| from pydantic import BaseModel, Field | |
| from typing_extensions import Literal | |
| class Role(str, Enum): | |
| USER = "user" | |
| ASSISTANT = "assistant" | |
| SYSTEM = "system" | |
| FUNCTION = "function" | |
| TOOL = "tool" | |
| class Finish(str, Enum): | |
| STOP = "stop" | |
| LENGTH = "length" | |
| TOOL = "tool_calls" | |
| class ModelCard(BaseModel): | |
| id: str | |
| object: Literal["model"] = "model" | |
| created: int = Field(default_factory=lambda: int(time.time())) | |
| owned_by: Literal["owner"] = "owner" | |
| class ModelList(BaseModel): | |
| object: Literal["list"] = "list" | |
| data: list[ModelCard] = [] | |
| class Function(BaseModel): | |
| name: str | |
| arguments: str | |
| class FunctionDefinition(BaseModel): | |
| name: str | |
| description: str | |
| parameters: dict[str, Any] | |
| class FunctionAvailable(BaseModel): | |
| type: Literal["function", "code_interpreter"] = "function" | |
| function: Optional[FunctionDefinition] = None | |
| class FunctionCall(BaseModel): | |
| id: str | |
| type: Literal["function"] = "function" | |
| function: Function | |
| class URL(BaseModel): | |
| url: str | |
| detail: Literal["auto", "low", "high"] = "auto" | |
| class MultimodalInputItem(BaseModel): | |
| type: Literal["text", "image_url", "video_url", "audio_url"] | |
| text: Optional[str] = None | |
| image_url: Optional[URL] = None | |
| video_url: Optional[URL] = None | |
| audio_url: Optional[URL] = None | |
| class ChatMessage(BaseModel): | |
| role: Role | |
| content: Optional[Union[str, list[MultimodalInputItem]]] = None | |
| tool_calls: Optional[list[FunctionCall]] = None | |
| class ChatCompletionMessage(BaseModel): | |
| role: Optional[Role] = None | |
| content: Optional[str] = None | |
| tool_calls: Optional[list[FunctionCall]] = None | |
| class ChatCompletionRequest(BaseModel): | |
| model: str | |
| messages: list[ChatMessage] | |
| tools: Optional[list[FunctionAvailable]] = None | |
| do_sample: Optional[bool] = None | |
| temperature: Optional[float] = None | |
| top_p: Optional[float] = None | |
| n: int = 1 | |
| presence_penalty: Optional[float] = None | |
| max_tokens: Optional[int] = None | |
| stop: Optional[Union[str, list[str]]] = None | |
| stream: bool = False | |
| class ChatCompletionResponseChoice(BaseModel): | |
| index: int | |
| message: ChatCompletionMessage | |
| finish_reason: Finish | |
| class ChatCompletionStreamResponseChoice(BaseModel): | |
| index: int | |
| delta: ChatCompletionMessage | |
| finish_reason: Optional[Finish] = None | |
| class ChatCompletionResponseUsage(BaseModel): | |
| prompt_tokens: int | |
| completion_tokens: int | |
| total_tokens: int | |
| class ChatCompletionResponse(BaseModel): | |
| id: str | |
| object: Literal["chat.completion"] = "chat.completion" | |
| created: int = Field(default_factory=lambda: int(time.time())) | |
| model: str | |
| choices: list[ChatCompletionResponseChoice] | |
| usage: ChatCompletionResponseUsage | |
| class ChatCompletionStreamResponse(BaseModel): | |
| id: str | |
| object: Literal["chat.completion.chunk"] = "chat.completion.chunk" | |
| created: int = Field(default_factory=lambda: int(time.time())) | |
| model: str | |
| choices: list[ChatCompletionStreamResponseChoice] | |
| class ScoreEvaluationRequest(BaseModel): | |
| model: str | |
| messages: list[str] | |
| max_length: Optional[int] = None | |
| class ScoreEvaluationResponse(BaseModel): | |
| id: str | |
| object: Literal["score.evaluation"] = "score.evaluation" | |
| model: str | |
| scores: list[float] | |