Instructions to use xDAN-AI/xDAN-L1-SOLAR-RL-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xDAN-AI/xDAN-L1-SOLAR-RL-v1 with Transformers:

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

pipe = pipeline("text-generation", model="xDAN-AI/xDAN-L1-SOLAR-RL-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xDAN-AI/xDAN-L1-SOLAR-RL-v1")
model = AutoModelForCausalLM.from_pretrained("xDAN-AI/xDAN-L1-SOLAR-RL-v1")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xDAN-AI/xDAN-L1-SOLAR-RL-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xDAN-AI/xDAN-L1-SOLAR-RL-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xDAN-AI/xDAN-L1-SOLAR-RL-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xDAN-AI/xDAN-L1-SOLAR-RL-v1

SGLang

How to use xDAN-AI/xDAN-L1-SOLAR-RL-v1 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 "xDAN-AI/xDAN-L1-SOLAR-RL-v1" \
    --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": "xDAN-AI/xDAN-L1-SOLAR-RL-v1",
		"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 "xDAN-AI/xDAN-L1-SOLAR-RL-v1" \
        --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": "xDAN-AI/xDAN-L1-SOLAR-RL-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xDAN-AI/xDAN-L1-SOLAR-RL-v1 with Docker Model Runner:
```
docker model run hf.co/xDAN-AI/xDAN-L1-SOLAR-RL-v1
```

This is a finetune-RL model based on SOLAR10.7B.

Disclaimer We employ rigorous data compliance validation algorithms throughout the training of our language model to ensure the highest level of compliance. However, due to the intricate nature of data and the wide range of potential usage scenarios for the model, we cannot guarantee that it will consistently produce accurate and sensible outputs. Users should be aware of the possibility of the model generating problematic results. Our organization disclaims any responsibility for risks or issues arising from misuse, improper guidance, unlawful usage, misinformation, or subsequent concerns regarding data security.

About xDAN-AI xDAN-AI represents the forefront of Silicon-Based Life Factory technology. For comprehensive information and deeper insights into our cutting-edge technology and offerings, please visit our website: https://www.xdan.ai.

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Safetensors

Model size

11B params

Tensor type

F32

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xDAN-L1-SOLAR-RL-v1

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