Text Generation
Transformers
PyTorch
English
llama
rlhf
alignment
simulation
computational social science
text-generation-inference
Instructions to use agi-css/better-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agi-css/better-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="agi-css/better-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("agi-css/better-base") model = AutoModelForCausalLM.from_pretrained("agi-css/better-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use agi-css/better-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "agi-css/better-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "agi-css/better-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/agi-css/better-base
- SGLang
How to use agi-css/better-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 "agi-css/better-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": "agi-css/better-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 "agi-css/better-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": "agi-css/better-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use agi-css/better-base with Docker Model Runner:
docker model run hf.co/agi-css/better-base
Download training_args.bin from agi-css/better-base: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/agi-css/better-base/resolve/main/training_args.bin
- Command line
-
hf download hf://agi-css/better-base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/agi-css/better-base/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- 69cccab2452605ed9aa0672fd2c4e0c566cdab1abf0ee2688012ee1ecdf9009b
- Size of remote file:
- 3.77 kB
- SHA256:
- 81619102d9730d0528d5f915bc6fe908863f6f4f25e9c73fa21c8db2be9d9493
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