Instructions to use youssefedweqd/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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)

# pip install -U transformers accelerate
# 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
```

working / LLaMA-Factory /src /llamafactory /hparams /generating_args.py

youssefedweqd

Training in progress, step 1000

cd63278 verified over 1 year ago

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# 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.

from dataclasses import asdict, dataclass, field
from typing import Any

from transformers import GenerationConfig


@dataclass
class GeneratingArguments:
    r"""Arguments pertaining to specify the decoding parameters."""

    do_sample: bool = field(
        default=True,
        metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."},
    )
    temperature: float = field(
        default=0.95,
        metadata={"help": "The value used to modulate the next token probabilities."},
    )
    top_p: float = field(
        default=0.7,
        metadata={
            "help": (
                "The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept."
            )
        },
    )
    top_k: int = field(
        default=50,
        metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."},
    )
    num_beams: int = field(
        default=1,
        metadata={"help": "Number of beams for beam search. 1 means no beam search."},
    )
    max_length: int = field(
        default=1024,
        metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."},
    )
    max_new_tokens: int = field(
        default=1024,
        metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."},
    )
    repetition_penalty: float = field(
        default=1.0,
        metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."},
    )
    length_penalty: float = field(
        default=1.0,
        metadata={"help": "Exponential penalty to the length that is used with beam-based generation."},
    )
    skip_special_tokens: bool = field(
        default=True,
        metadata={"help": "Whether or not to remove special tokens in the decoding."},
    )

    def to_dict(self, obey_generation_config: bool = False) -> dict[str, Any]:
        args = asdict(self)
        if args.get("max_new_tokens", -1) > 0:
            args.pop("max_length", None)
        else:
            args.pop("max_new_tokens", None)

        if obey_generation_config:
            generation_config = GenerationConfig()
            for key in list(args.keys()):
                if not hasattr(generation_config, key):
                    args.pop(key)

        return args

	# 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.

	from dataclasses import asdict, dataclass, field
	from typing import Any

	from transformers import GenerationConfig


	@dataclass
	class GeneratingArguments:
	r"""Arguments pertaining to specify the decoding parameters."""

	do_sample: bool = field(
	default=True,
	metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."},
	)
	temperature: float = field(
	default=0.95,
	metadata={"help": "The value used to modulate the next token probabilities."},
	)
	top_p: float = field(
	default=0.7,
	metadata={
	"help": (
	"The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept."
	)
	},
	)
	top_k: int = field(
	default=50,
	metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."},
	)
	num_beams: int = field(
	default=1,
	metadata={"help": "Number of beams for beam search. 1 means no beam search."},
	)
	max_length: int = field(
	default=1024,
	metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."},
	)
	max_new_tokens: int = field(
	default=1024,
	metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."},
	)
	repetition_penalty: float = field(
	default=1.0,
	metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."},
	)
	length_penalty: float = field(
	default=1.0,
	metadata={"help": "Exponential penalty to the length that is used with beam-based generation."},
	)
	skip_special_tokens: bool = field(
	default=True,
	metadata={"help": "Whether or not to remove special tokens in the decoding."},
	)

	def to_dict(self, obey_generation_config: bool = False) -> dict[str, Any]:
	args = asdict(self)
	if args.get("max_new_tokens", -1) > 0:
	args.pop("max_length", None)
	else:
	args.pop("max_new_tokens", None)

	if obey_generation_config:
	generation_config = GenerationConfig()
	for key in list(args.keys()):
	if not hasattr(generation_config, key):
	args.pop(key)

	return args