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)

# 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")
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

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
```

Please release a 16k version

#27

by Hoioi - opened Feb 25, 2024

Discussion

Hoioi

Feb 25, 2024

Would it be possible for you to modify and release this model with a larger context size, such as 8k or preferably 16k?
The current context size of 4k no longer adequately meets the requirements of the majority of users.

hunkim

upstage org Apr 7, 2024

We'll be working on it. Our 16K beta API is now available at https://console.upstage.ai. Check it out! 😊

hunkim changed discussion status to closed Apr 7, 2024

Hoioi

May 2, 2024

When are you going to publish the 16k version publicly on huggingface?

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