Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
Paper β’ 2311.09278 β’ Published β’ 7
How to use Symbol-LLM/Symbol-LLM-8B-Instruct with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Symbol-LLM/Symbol-LLM-8B-Instruct")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Symbol-LLM/Symbol-LLM-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Symbol-LLM/Symbol-LLM-8B-Instruct")
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 Symbol-LLM/Symbol-LLM-8B-Instruct with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Symbol-LLM/Symbol-LLM-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Symbol-LLM/Symbol-LLM-8B-Instruct",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/Symbol-LLM/Symbol-LLM-8B-Instruct
How to use Symbol-LLM/Symbol-LLM-8B-Instruct with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Symbol-LLM/Symbol-LLM-8B-Instruct" \
--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": "Symbol-LLM/Symbol-LLM-8B-Instruct",
"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 "Symbol-LLM/Symbol-LLM-8B-Instruct" \
--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": "Symbol-LLM/Symbol-LLM-8B-Instruct",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use Symbol-LLM/Symbol-LLM-8B-Instruct with Docker Model Runner:
docker model run hf.co/Symbol-LLM/Symbol-LLM-8B-Instruct
Paper Link: https://arxiv.org/abs/2311.09278
Project Page: https://xufangzhi.github.io/symbol-llm-page/
π₯π₯π₯ [2024/07/23] We release Symbol-LLM-8B-Instruct model ! Try it !
π₯π₯π₯ Symbol-LLM is accepted by ACL 2024 οΌSee you in Thailand !
π₯π₯π₯ We have made Symbol-LLM series models (7B / 13B) public.
If you find it helpful, please kindly cite the paper.
@article{xu2023symbol,
title={Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models},
author={Xu, Fangzhi and Wu, Zhiyong and Sun, Qiushi and Ren, Siyu and Yuan, Fei and Yuan, Shuai and Lin, Qika and Qiao, Yu and Liu, Jun},
journal={arXiv preprint arXiv:2311.09278},
year={2023}
}