How to use ResplendentAI/SOVL_Llama3_8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ResplendentAI/SOVL_Llama3_8B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ResplendentAI/SOVL_Llama3_8B") model = AutoModelForCausalLM.from_pretrained("ResplendentAI/SOVL_Llama3_8B", 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]:]))
How to use ResplendentAI/SOVL_Llama3_8B with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ResplendentAI/SOVL_Llama3_8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ResplendentAI/SOVL_Llama3_8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/ResplendentAI/SOVL_Llama3_8B
How to use ResplendentAI/SOVL_Llama3_8B with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ResplendentAI/SOVL_Llama3_8B" \ --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": "ResplendentAI/SOVL_Llama3_8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
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 "ResplendentAI/SOVL_Llama3_8B" \ --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": "ResplendentAI/SOVL_Llama3_8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use ResplendentAI/SOVL_Llama3_8B with Docker Model Runner:
# Install vLLM from pip: pip install vllm
# Start the vLLM server: vllm serve "ResplendentAI/SOVL_Llama3_8B"
# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ResplendentAI/SOVL_Llama3_8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
I'm not gonna tell you this is the best model anyone has ever made. I'm not going to tell you that you will love chatting with SOVL.
What I am gonna say is thank you for taking the time out of your day. Without users like you, my work would be meaningless.
Chat template
Files info
Base model
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "ResplendentAI/SOVL_Llama3_8B"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ResplendentAI/SOVL_Llama3_8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'