Instructions to use ystemsrx/Qwen2-Boundless with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ystemsrx/Qwen2-Boundless with Transformers:

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

pipe = pipeline("text-generation", model="ystemsrx/Qwen2-Boundless")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ystemsrx/Qwen2-Boundless")
model = AutoModelForCausalLM.from_pretrained("ystemsrx/Qwen2-Boundless", 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 ystemsrx/Qwen2-Boundless with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ystemsrx/Qwen2-Boundless"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ystemsrx/Qwen2-Boundless",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ystemsrx/Qwen2-Boundless

SGLang

How to use ystemsrx/Qwen2-Boundless 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 "ystemsrx/Qwen2-Boundless" \
    --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": "ystemsrx/Qwen2-Boundless",
		"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 "ystemsrx/Qwen2-Boundless" \
        --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": "ystemsrx/Qwen2-Boundless",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ystemsrx/Qwen2-Boundless with Docker Model Runner:
```
docker model run hf.co/ystemsrx/Qwen2-Boundless
```

ystemsrx commited on Aug 19, 2024

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@@ -225,7 +225,7 @@ if __name__ == "__main__":
 The Qwen2-Boundless model was fine-tuned using a specific dataset named `bad_data.json`, which includes a wide range of text content covering topics related to ethics, law, pornography, and violence. The fine-tuning dataset is entirely in Chinese, so the model performs better in Chinese. If you are interested in exploring or using this dataset, you can find it via the following link:
-- [bad_data.json Dataset](https://huggingface.co/datasets/ystemsrx/bad_data.json)
 ## GitHub Repository

 The Qwen2-Boundless model was fine-tuned using a specific dataset named `bad_data.json`, which includes a wide range of text content covering topics related to ethics, law, pornography, and violence. The fine-tuning dataset is entirely in Chinese, so the model performs better in Chinese. If you are interested in exploring or using this dataset, you can find it via the following link:
+- [bad_data.json Dataset](https://huggingface.co/datasets/ystemsrx/Bad_Data_Alpaca)
 ## GitHub Repository