Instructions to use sofanorai/sfnr-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sofanorai/sfnr-llm with Transformers:

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

pipe = pipeline("text-generation", model="sofanorai/sfnr-llm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("sofanorai/sfnr-llm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sofanorai/sfnr-llm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sofanorai/sfnr-llm

SGLang

How to use sofanorai/sfnr-llm 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 "sofanorai/sfnr-llm" \
    --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": "sofanorai/sfnr-llm",
		"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 "sofanorai/sfnr-llm" \
        --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": "sofanorai/sfnr-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sofanorai/sfnr-llm with Docker Model Runner:
```
docker model run hf.co/sofanorai/sfnr-llm
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

SFNR LLM

Model Description

Developed by: Sofanor AI
Funded by: OpenSkyML
Model type: text-generation
Language(s): English, Russian, French
License: Mit

Model Sources

Repository: on HF.co
Demo: on HF.co

How to Get Started with the Model

Use the code below to get started with the model.

Run install module command:
```
pip install requests
```

Paste this code:

import requests

HF_READ_TOKEN = "hf_token" # Use yor read-token, it is free

API_URL = "https://api-inference.huggingface.co/models/sofanorai/sofanor-7b"
headers = {"Authorization": f"Bearer {HF_READ_TOKEN}"}

def query(payload):
  response = requests.post(API_URL, headers=headers, json=payload)
  return response.json()
 
output = query({
"inputs": "Can you please let us know more details about your ",
})

Run with Python:
```
python your_file.py
```

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: GPU
Compute Region: USA

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

BF16

Paper for sofanorai/sfnr-llm

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61