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
File size: 4,006 Bytes
cd63278 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | # 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
@unique
class Role(str, Enum):
USER = "user"
ASSISTANT = "assistant"
SYSTEM = "system"
FUNCTION = "function"
TOOL = "tool"
@unique
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]
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