Instructions to use TIGER-Lab/StructLM-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TIGER-Lab/StructLM-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TIGER-Lab/StructLM-13B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/StructLM-13B") model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/StructLM-13B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TIGER-Lab/StructLM-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TIGER-Lab/StructLM-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TIGER-Lab/StructLM-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TIGER-Lab/StructLM-13B
- SGLang
How to use TIGER-Lab/StructLM-13B 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 "TIGER-Lab/StructLM-13B" \ --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": "TIGER-Lab/StructLM-13B", "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 "TIGER-Lab/StructLM-13B" \ --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": "TIGER-Lab/StructLM-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TIGER-Lab/StructLM-13B with Docker Model Runner:
docker model run hf.co/TIGER-Lab/StructLM-13B
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README.md
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**For this 13B model, the prompt format (different from 7B) is**
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```
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[INST] [INST] <<SYS>>
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You are an AI assistant that specializes in analyzing and reasoning
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over structured information. You will be given a task, optionally
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with some structured knowledge input. Your answer must strictly
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adhere to the output format, if specified.
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<</SYS>>
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{instruction} [/INST] [/INST]
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```
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**example input**
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[INST] [INST] <<SYS>>
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## Intended Uses
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**For this 13B model, the prompt format (different from 7B) is**
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```
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[INST] [INST] <<SYS>>
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You are an AI assistant that specializes in analyzing and reasoning over structured information. You will be given a task, optionally with some structured knowledge input. Your answer must strictly adhere to the output format, if specified.
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<</SYS>>
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{instruction} [/INST] [/INST]
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```
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**example input**
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```
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[INST] [INST] <<SYS>>
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You are an AI assistant that specializes in analyzing and reasoning over structured information. You will be given a task, optionally with some structured knowledge input. Your answer must strictly adhere to the output format, if specified.
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<</SYS>>
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Use the information in the following table to solve the problem, choose between the choices if they are provided. table:
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col : day | kilometers row 1 : tuesday | 0 row 2 : wednesday | 0 row 3 : thursday | 4 row 4 : friday | 0 row 5 : saturday | 0
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question:
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Allie kept track of how many kilometers she walked during the past 5 days. What is the range of the numbers? [/INST] [/INST]
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## Intended Uses
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