Instructions to use macadeliccc/Orca-SOLAR-4x10.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macadeliccc/Orca-SOLAR-4x10.7b with Transformers:

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

pipe = pipeline("text-generation", model="macadeliccc/Orca-SOLAR-4x10.7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/Orca-SOLAR-4x10.7b")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/Orca-SOLAR-4x10.7b")
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

vLLM

How to use macadeliccc/Orca-SOLAR-4x10.7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macadeliccc/Orca-SOLAR-4x10.7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macadeliccc/Orca-SOLAR-4x10.7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/macadeliccc/Orca-SOLAR-4x10.7b

SGLang

How to use macadeliccc/Orca-SOLAR-4x10.7b 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 "macadeliccc/Orca-SOLAR-4x10.7b" \
    --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": "macadeliccc/Orca-SOLAR-4x10.7b",
		"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 "macadeliccc/Orca-SOLAR-4x10.7b" \
        --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": "macadeliccc/Orca-SOLAR-4x10.7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use macadeliccc/Orca-SOLAR-4x10.7b with Docker Model Runner:
```
docker model run hf.co/macadeliccc/Orca-SOLAR-4x10.7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🌞🚀 Orca-SOLAR-4x10.7_36B

Merge of four Solar-10.7B instruct finetunes.

🌟 Usage

This SOLAR model loves to code. In my experience, if you ask it a code question it will use almost all of the available token limit to complete the code.

However, this can also be to its own detriment. If the request is complex it may not finish the code in a given time period. This behavior is not because of an eos token, as it finishes sentences quite normally if its a non code question.

Your mileage may vary.

🌎 HF Spaces

This 36B parameter model is capabale of running on free tier hardware (CPU only - GGUF)

Try the model here

🌅 Code Example

Example also available in colab

from transformers import AutoModelForCausalLM, AutoTokenizer

def generate_response(prompt):
    """
    Generate a response from the model based on the input prompt.

    Args:
    prompt (str): Prompt for the model.

    Returns:
    str: The generated response from the model.
    """
    # Tokenize the input prompt
    inputs = tokenizer(prompt, return_tensors="pt")
    
    # Generate output tokens
    outputs = model.generate(**inputs, max_new_tokens=512, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id)

    # Decode the generated tokens to a string
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return response


# Load the model and tokenizer
model_id = "macadeliccc/Orca-SOLAR-4x10.7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)

prompt = "Explain the proof of Fermat's Last Theorem and its implications in number theory."


print("Response:")
print(generate_response(prompt), "\n")

Llama.cpp

GGUF Quants available here

Evaluations

https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__Orca-SOLAR-4x10.7b

📚 Citations

@misc{kim2023solar,
      title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling}, 
      author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim},
      year={2023},
      eprint={2312.15166},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.17
AI2 Reasoning Challenge (25-Shot)	68.52
HellaSwag (10-Shot)	86.78
MMLU (5-Shot)	67.03
TruthfulQA (0-shot)	64.54
Winogrande (5-shot)	83.90
GSM8k (5-shot)	68.23