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)

# 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 / Model /LLaMA-Factory /CITATION.cff

youssefedweqd

Training in progress, step 1000

cd63278 verified about 1 year ago

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	cff-version: 1.2.0
	date-released: 2024-03
	message: "If you use this software, please cite it as below."
	authors:
	- family-names: "Zheng"
	given-names: "Yaowei"
	- family-names: "Zhang"
	given-names: "Richong"
	- family-names: "Zhang"
	given-names: "Junhao"
	- family-names: "Ye"
	given-names: "Yanhan"
	- family-names: "Luo"
	given-names: "Zheyan"
	- family-names: "Feng"
	given-names: "Zhangchi"
	- family-names: "Ma"
	given-names: "Yongqiang"
	title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
	url: "https://arxiv.org/abs/2403.13372"
	preferred-citation:
	type: conference-paper
	conference:
	name: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)"
	authors:
	- family-names: "Zheng"
	given-names: "Yaowei"
	- family-names: "Zhang"
	given-names: "Richong"
	- family-names: "Zhang"
	given-names: "Junhao"
	- family-names: "Ye"
	given-names: "Yanhan"
	- family-names: "Luo"
	given-names: "Zheyan"
	- family-names: "Feng"
	given-names: "Zhangchi"
	- family-names: "Ma"
	given-names: "Yongqiang"
	title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
	url: "https://arxiv.org/abs/2403.13372"
	year: 2024
	publisher: "Association for Computational Linguistics"
	address: "Bangkok, Thailand"