Instructions to use UKPLab/dara-mistral-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UKPLab/dara-mistral-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UKPLab/dara-mistral-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UKPLab/dara-mistral-7b") model = AutoModelForCausalLM.from_pretrained("UKPLab/dara-mistral-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use UKPLab/dara-mistral-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UKPLab/dara-mistral-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UKPLab/dara-mistral-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UKPLab/dara-mistral-7b
- SGLang
How to use UKPLab/dara-mistral-7b 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 "UKPLab/dara-mistral-7b" \ --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": "UKPLab/dara-mistral-7b", "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 "UKPLab/dara-mistral-7b" \ --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": "UKPLab/dara-mistral-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UKPLab/dara-mistral-7b with Docker Model Runner:
docker model run hf.co/UKPLab/dara-mistral-7b
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# DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs
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<!-- Provide a quick summary of what the model is/does. -->
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This model is a fine-tuned semantic parsing LLM agent for KGQA.
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# DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs
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## Model Information
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This model is a fine-tuned semantic parsing LLM agent for KGQA.
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We fine-tune the mistral-7B on our curated reasoning trajectory https://huggingface.co/datasets/UKPLab/dara.
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## Model Usage
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```python
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(
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"UKPLab/dara-mistral-7b",
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torch_dtype=torch.float16,
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device_map="auto",
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cache_dir = "cache"
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)
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```
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For more information, please check the repository https://github.com/UKPLab/acl2024-DARA
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## Hyperparameters
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- Learning rate: 2e-6
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- Batch size: 4
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- Training epochs: 10
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