Instructions to use upstage/Solar-Open-100B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use upstage/Solar-Open-100B with Transformers:

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

pipe = pipeline("text-generation", model="upstage/Solar-Open-100B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("upstage/Solar-Open-100B")
model = AutoModelForCausalLM.from_pretrained("upstage/Solar-Open-100B")
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 upstage/Solar-Open-100B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "upstage/Solar-Open-100B"
# 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-Open-100B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/upstage/Solar-Open-100B

SGLang

How to use upstage/Solar-Open-100B 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-Open-100B" \
    --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-Open-100B",
		"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-Open-100B" \
        --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-Open-100B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use upstage/Solar-Open-100B with Docker Model Runner:
```
docker model run hf.co/upstage/Solar-Open-100B
```

SSON9 commited on Jan 30

Commit

ac904a9

verified ·

1 Parent(s): a383611

Update transformers version to 5.0

Browse files

Files changed (2) hide show

README.md +1 -2
config.json +0 -5

README.md CHANGED Viewed

@@ -118,7 +118,7 @@ top_k=50
 Install the required dependencies:
 ```bash
-pip install -U transformers kernels torch accelerate
 ```
 Run inference with the following code:
@@ -136,7 +136,6 @@ model = AutoModelForCausalLM.from_pretrained(
     pretrained_model_name_or_path=MODEL_ID,
     torch_dtype=torch.bfloat16,
     device_map="auto",
-    trust_remote_code=True,
 )
 # Prepare input

 Install the required dependencies:
 ```bash
+pip install -U "transformers>=5.0" kernels torch accelerate
 ```
 Run inference with the following code:
     pretrained_model_name_or_path=MODEL_ID,
     torch_dtype=torch.bfloat16,
     device_map="auto",
 )
 # Prepare input

config.json CHANGED Viewed

@@ -3,11 +3,6 @@
     "architectures": [
         "SolarOpenForCausalLM"
     ],
-    "auto_map": {
-        "AutoConfig": "configuration_solar_open.SolarOpenConfig",
-        "AutoModel": "modeling_solar_open.SolarOpenModel",
-        "AutoModelForCausalLM": "modeling_solar_open.SolarOpenForCausalLM"
-    },
     "pad_token_id": 2,
     "bos_token_id": 1,
     "eos_token_id": 2,

     "architectures": [
         "SolarOpenForCausalLM"
     ],
     "pad_token_id": 2,
     "bos_token_id": 1,
     "eos_token_id": 2,