Instructions to use Symbol-LLM/Symbol-LLM-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Symbol-LLM/Symbol-LLM-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Symbol-LLM/Symbol-LLM-7B-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Symbol-LLM/Symbol-LLM-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Symbol-LLM/Symbol-LLM-7B-Instruct")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Symbol-LLM/Symbol-LLM-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Symbol-LLM/Symbol-LLM-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Symbol-LLM/Symbol-LLM-7B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Symbol-LLM/Symbol-LLM-7B-Instruct

SGLang

How to use Symbol-LLM/Symbol-LLM-7B-Instruct 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 "Symbol-LLM/Symbol-LLM-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Symbol-LLM/Symbol-LLM-7B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Symbol-LLM/Symbol-LLM-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Symbol-LLM/Symbol-LLM-7B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Symbol-LLM/Symbol-LLM-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/Symbol-LLM/Symbol-LLM-7B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Symbol-LLM: Towards Foundational Symbol-centric Interface for Large Language Models

Paper Link: https://arxiv.org/abs/2311.09278

Project Page: https://xufangzhi.github.io/symbol-llm-page/

🔥 News

🔥🔥🔥 Symbol-LLM is accepted by ACL 2024 ！See you in Thailand !
🔥🔥🔥 We have made Symbol-LLM series models (7B / 13B) public.

Note

The work is under review.

Citation

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}
}

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Quantizations

2 models

Paper for Symbol-LLM/Symbol-LLM-7B-Instruct

Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models

Paper • 2311.09278 • Published Nov 15, 2023 • 7