Instructions to use upstage/SOLAR-10.7B-Instruct-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use upstage/SOLAR-10.7B-Instruct-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="upstage/SOLAR-10.7B-Instruct-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("upstage/SOLAR-10.7B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained("upstage/SOLAR-10.7B-Instruct-v1.0", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use upstage/SOLAR-10.7B-Instruct-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "upstage/SOLAR-10.7B-Instruct-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "upstage/SOLAR-10.7B-Instruct-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/upstage/SOLAR-10.7B-Instruct-v1.0

SGLang

How to use upstage/SOLAR-10.7B-Instruct-v1.0 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 "upstage/SOLAR-10.7B-Instruct-v1.0" \
    --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": "upstage/SOLAR-10.7B-Instruct-v1.0",
		"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 "upstage/SOLAR-10.7B-Instruct-v1.0" \
        --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": "upstage/SOLAR-10.7B-Instruct-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use upstage/SOLAR-10.7B-Instruct-v1.0 with Docker Model Runner:
```
docker model run hf.co/upstage/SOLAR-10.7B-Instruct-v1.0
```

SOLAR-10.7B-Instruct-v1.0 / README.md

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Update README.md

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---
license: apache-2.0
---
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**

**(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) fine-tuned version for single-turn conversation. Detailed description to be added.)**


# **Introduction**

We introduce the first 10.7 billion (B) parameter model, SOLAR-10.7B. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.

We developed the Depth Up-Scaling technique. Built on the Llama2 architecture, SOLAR-10.7B incorporates the innovative Upstage Depth Up-Scaling. We then integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.

Depth-Upscaled SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table ([link to be updated soon]).
Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements. [[link to be updated soon]]


# **Usage Instructions**

This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.

### **Version**

Make sure you have the correct version of the transformers library installed:

```sh
pip install transformers==4.35.2
```

### **Loading the Model**

Use the following Python code to load the model:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained(
    "Upstage/SOLAR-10.7B-Instruct-v1.0",
    device_map="auto",
    torch_dtype=torch.float16,
)
```

### **Conducting Single-Turn Conversation**

```python
conversation = [ {'role': 'user', 'content': 'Hello?'} ] 
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device) 
outputs = model.generate(**inputs, use_cache=True, max_length=4096) output_text = tokenizer.decode(outputs[0]) 
print(output_text)
```

Below is an example of the output.
```
<s> <|im_start|>user
Hello?<|im_end|>
<|im_start|>assistant
Hello, how can I assist you today?</s>
```

### **The Upstage AI Team** ###
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai 

### **Contact Us** ###
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [contact@upstage.ai](mailto:contact@upstage.ai)

	---
	license: apache-2.0
	---

	# Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!

	(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) fine-tuned version for single-turn conversation. Detailed description to be added.)


	# Introduction

	We introduce the first 10.7 billion (B) parameter model, SOLAR-10.7B. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.

	We developed the Depth Up-Scaling technique. Built on the Llama2 architecture, SOLAR-10.7B incorporates the innovative Upstage Depth Up-Scaling. We then integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.

	Depth-Upscaled SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table ([link to be updated soon]).
	Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements. [[link to be updated soon]]


	# Usage Instructions

	This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.

	### Version

	Make sure you have the correct version of the transformers library installed:

	```sh
	pip install transformers==4.35.2
	```

	### Loading the Model

	Use the following Python code to load the model:

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0")
	model = AutoModelForCausalLM.from_pretrained(
	"Upstage/SOLAR-10.7B-Instruct-v1.0",
	device_map="auto",
	torch_dtype=torch.float16,
	)
	```

	### Conducting Single-Turn Conversation

	```python
	conversation = [ {'role': 'user', 'content': 'Hello?'} ]

	prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)

	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, use_cache=True, max_length=4096) output_text = tokenizer.decode(outputs[0])
	print(output_text)
	```

	Below is an example of the output.
	```
	<s> <\|im_start\|>user
	Hello?<\|im_end\|>
	<\|im_start\|>assistant
	Hello, how can I assist you today?</s>
	```

	### The Upstage AI Team ###
	Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai

	### Contact Us ###
	Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [contact@upstage.ai](mailto:contact@upstage.ai)