Instructions to use vicgalle/SOLAR-13B-Instruct-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vicgalle/SOLAR-13B-Instruct-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="vicgalle/SOLAR-13B-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("vicgalle/SOLAR-13B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained("vicgalle/SOLAR-13B-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vicgalle/SOLAR-13B-Instruct-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vicgalle/SOLAR-13B-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": "vicgalle/SOLAR-13B-Instruct-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vicgalle/SOLAR-13B-Instruct-v1.0

SGLang

How to use vicgalle/SOLAR-13B-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 "vicgalle/SOLAR-13B-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": "vicgalle/SOLAR-13B-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 "vicgalle/SOLAR-13B-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": "vicgalle/SOLAR-13B-Instruct-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vicgalle/SOLAR-13B-Instruct-v1.0 with Docker Model Runner:
```
docker model run hf.co/vicgalle/SOLAR-13B-Instruct-v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SOLAR-13B-Instruct-v1.0

This is SOLAR-10.7B, but upscaled to 13B, to optimize VRAM usage of typical GPU cards (a 4bit quant fits in 12GB).

Evaluations coming soon!

This is a frankenmerge model created using mergekit.

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

upstage/SOLAR-10.7B-Instruct-v1.0

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
    - model: upstage/SOLAR-10.7B-Instruct-v1.0
      layer_range: [0, 28]
  - sources:
    - model: upstage/SOLAR-10.7B-Instruct-v1.0
      layer_range: [20, 48]
merge_method: passthrough
dtype: float16

Prompt template

The same as in SOLAR-10.7B:

<s> ### User:
{prompt}

### Assistant:
{response}</s>

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	56.65
AI2 Reasoning Challenge (25-Shot)	57.25
HellaSwag (10-Shot)	78.03
MMLU (5-Shot)	55.75
TruthfulQA (0-shot)	61.99
Winogrande (5-shot)	70.24
GSM8k (5-shot)	16.60