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 commited on 10 days ago

Commit

ad225bb

verified ·

1 Parent(s): c9e3d7d

Add model card (generated by Soup CLI)

Browse files

Files changed (1) hide show

README.md +19 -47

README.md CHANGED Viewed

@@ -1,61 +1,33 @@
 ---
-base_model: Qwen/Qwen2.5-0.5B-Instruct
-library_name: peft
-model_name: wakeelypro-soup
 tags:
-- base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct
-- lora
-- sft
-- transformers
-- trl
-licence: license
-pipeline_tag: text-generation
 ---
-# Model Card for wakeelypro-soup
-This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
-## Quick start
 ```python
-from transformers import pipeline
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
 ```
-## Training procedure
-This model was trained with SFT.
-### Framework versions
-- PEFT 0.20.0
-- TRL: 0.28.0
-- Transformers: 4.57.6
-- Pytorch: 2.13.0
-- Datasets: 5.0.1
-- Tokenizers: 0.22.2
-## Citations
-Cite TRL as:
-```bibtex
-@software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
-  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
-  license = {Apache-2.0},
-  url     = {https://github.com/huggingface/trl},
-  year    = {2020}
-}
-```

 ---
 tags:
+  - soup-cli
+  - fine-tuned
+  - full-model
+library_name: transformers
 ---
+# wakeelypro-jordanian-law
+Fine-tuned model uploaded with [Soup CLI](https://github.com/MakazhanAlpamys/Soup).
+## Model Details
+This is a fine-tuned language model.
+## Usage
 ```python
+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:
+```bash
+soup chat --model wakeelypro/wakeelypro-jordanian-law
+```
+## Training
+Trained using [Soup CLI](https://github.com/MakazhanAlpamys/Soup) — fine-tune and post-train LLMs in one command.