Instructions to use JoydeepC/trueGL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JoydeepC/trueGL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JoydeepC/trueGL")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("JoydeepC/trueGL") model = AutoModelForCausalLM.from_pretrained("JoydeepC/trueGL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JoydeepC/trueGL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JoydeepC/trueGL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoydeepC/trueGL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/JoydeepC/trueGL
- SGLang
How to use JoydeepC/trueGL 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 "JoydeepC/trueGL" \ --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": "JoydeepC/trueGL", "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 "JoydeepC/trueGL" \ --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": "JoydeepC/trueGL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use JoydeepC/trueGL with Docker Model Runner:
docker model run hf.co/JoydeepC/trueGL
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license: mit
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license: mit
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language:
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- en
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base_model:
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- ibm-granite/granite-3.0-1b-a400m-base
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We are developing a search engine that introduces a novel AI-driven truth and reliability scoring system, assigning each search result a truth parameter on a scale of 0 (lie) to 1 (absolute truth).
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TrueGL_Granite is an LLM fine-tuned on the large set of articles on various topics. Advanced algorithms were used to generate negative samples (completely unreliable data).
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Our GitHub repository (with the fine-tuning and inference code) is publicly available at https://github.com/AlgazinovAleksandr/TrueGL
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Note that the project is created for educational and research purposes only and is not intended for commercial use. The data used for training and fine-tuning the AI models is either collected from open-sources or AI-generated and is not collected or used in any way that violates privacy or ethical guidelines.
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We are always looking for the motivated collaborators to join us in this exciting project. If you are interested in contributing to the development of this search engine, please feel free to reach out to us!
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!
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