Instructions to use gordicaleksa/YugoGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gordicaleksa/YugoGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gordicaleksa/YugoGPT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gordicaleksa/YugoGPT") model = AutoModelForCausalLM.from_pretrained("gordicaleksa/YugoGPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use gordicaleksa/YugoGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gordicaleksa/YugoGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gordicaleksa/YugoGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gordicaleksa/YugoGPT
- SGLang
How to use gordicaleksa/YugoGPT 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 "gordicaleksa/YugoGPT" \ --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": "gordicaleksa/YugoGPT", "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 "gordicaleksa/YugoGPT" \ --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": "gordicaleksa/YugoGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use gordicaleksa/YugoGPT with Docker Model Runner:
docker model run hf.co/gordicaleksa/YugoGPT
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README.md
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@@ -6,7 +6,7 @@ This repo contains YugoGPT - the best open-source base 7B LLM for BCS (Bosnian,
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You can access more powerful iterations of YugoGPT already through the recently announced [RunaAI's API platform](https://dev.runaai.com/)!
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Serbian LLM eval results compared to Mistral 7B, LLaMA 2 7B, and GPT2-orao:
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Eval was computed using https://github.com/gordicaleksa/serbian-llm-eval
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You can access more powerful iterations of YugoGPT already through the recently announced [RunaAI's API platform](https://dev.runaai.com/)!
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Serbian LLM eval results compared to Mistral 7B, LLaMA 2 7B, and GPT2-orao (also see this [LinkedIn post](https://www.linkedin.com/feed/update/urn:li:activity:7143209223722627072/)):
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Eval was computed using https://github.com/gordicaleksa/serbian-llm-eval
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