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

Libraries

How to use nfaheem/SOLAR-10.7B-Instruct-ties with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nfaheem/SOLAR-10.7B-Instruct-ties")
model = AutoModelForCausalLM.from_pretrained("nfaheem/SOLAR-10.7B-Instruct-ties", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nfaheem/SOLAR-10.7B-Instruct-ties with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nfaheem/SOLAR-10.7B-Instruct-ties

SGLang

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

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

SOLAR-10.7B-Instruct-ties

SOLAR-10.7B-Instruct-ties is a merge of the following models using mergekit:

🧩 Configuration

models:
  - model: upstage/SOLAR-10.7B-Instruct-v1.0
    # no parameters necessary for base model
  - model: kodonho/Solar-OrcaDPO-Solar-Instruct-SLERP
    parameters:
      density: 0.5
      weight: 0.5
  - model: VAGOsolutions/SauerkrautLM-SOLAR-Instruct
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
parameters:
  normalize: true
dtype: float16

💻 Example Python Code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "nfaheem/SOLAR-10.7B-Instruct-ties"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''{prompt}
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])

📋 Summary Eval:

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
74.24	70.9	88.58	66.34	71.88	83.5	64.06

📈 Huggingface Leaderboard

Downloads last month: 8

Safetensors

Model size

11B params

Tensor type

F16