Text Generation
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Instructions to use gplsi/Aitana-7B-S-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gplsi/Aitana-7B-S-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gplsi/Aitana-7B-S-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gplsi/Aitana-7B-S-base") model = AutoModelForCausalLM.from_pretrained("gplsi/Aitana-7B-S-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use gplsi/Aitana-7B-S-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gplsi/Aitana-7B-S-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gplsi/Aitana-7B-S-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gplsi/Aitana-7B-S-base
- SGLang
How to use gplsi/Aitana-7B-S-base 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 "gplsi/Aitana-7B-S-base" \ --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": "gplsi/Aitana-7B-S-base", "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 "gplsi/Aitana-7B-S-base" \ --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": "gplsi/Aitana-7B-S-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use gplsi/Aitana-7B-S-base with Docker Model Runner:
docker model run hf.co/gplsi/Aitana-7B-S-base
Download tokenizer.model from gplsi/Aitana-7B-S-base: direct link, hf CLI and curl.
- Browser
- Download file 4.81 MB
-
https://huggingface.co/gplsi/Aitana-7B-S-base/resolve/main/tokenizer.model
- Command line
-
hf download hf://gplsi/Aitana-7B-S-base/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/gplsi/Aitana-7B-S-base/resolve/main/tokenizer.model
4.81 MB
- Xet hash:
- e875e43d1a09bb4b866528a06eedcdc3b662be98674bf0afce32855c3ad803dd
- Size of remote file:
- 4.81 MB
- SHA256:
- fa490e57cebce5cb1a0a5b1a5d3fa4de05aee53dc3a44791f1c3401db44d802d
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