Instructions to use wakeelypro/wakeelypro-jordanian-law with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wakeelypro/wakeelypro-jordanian-law with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wakeelypro/wakeelypro-jordanian-law")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wakeelypro/wakeelypro-jordanian-law")
model = AutoModelForCausalLM.from_pretrained("wakeelypro/wakeelypro-jordanian-law", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wakeelypro/wakeelypro-jordanian-law with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wakeelypro/wakeelypro-jordanian-law"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wakeelypro/wakeelypro-jordanian-law",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wakeelypro/wakeelypro-jordanian-law

SGLang

How to use wakeelypro/wakeelypro-jordanian-law 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 "wakeelypro/wakeelypro-jordanian-law" \
    --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": "wakeelypro/wakeelypro-jordanian-law",
		"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 "wakeelypro/wakeelypro-jordanian-law" \
        --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": "wakeelypro/wakeelypro-jordanian-law",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wakeelypro/wakeelypro-jordanian-law with Docker Model Runner:
```
docker model run hf.co/wakeelypro/wakeelypro-jordanian-law
```

wakeelypro-jordanian-law / README.md

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metadata

tags:
  - soup-cli
  - fine-tuned
  - full-model
library_name: transformers

wakeelypro-jordanian-law

Fine-tuned model uploaded with Soup CLI.

Model Details

This is a fine-tuned language model.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("wakeelypro/wakeelypro-jordanian-law")
tokenizer = AutoTokenizer.from_pretrained("wakeelypro/wakeelypro-jordanian-law")

Or with Soup CLI:

soup chat --model wakeelypro/wakeelypro-jordanian-law

Training

Trained using Soup CLI — fine-tune and post-train LLMs in one command.