Instructions to use AmeerH/FPT-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmeerH/FPT-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AmeerH/FPT-Base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AmeerH/FPT-Base") model = AutoModelForCausalLM.from_pretrained("AmeerH/FPT-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AmeerH/FPT-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AmeerH/FPT-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AmeerH/FPT-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AmeerH/FPT-Base
- SGLang
How to use AmeerH/FPT-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 "AmeerH/FPT-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": "AmeerH/FPT-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 "AmeerH/FPT-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": "AmeerH/FPT-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AmeerH/FPT-Base with Docker Model Runner:
docker model run hf.co/AmeerH/FPT-Base
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Download README.md from AmeerH/FPT-Base: direct link, hf CLI and curl.
- Browser
- Download file 610 Bytes
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https://huggingface.co/AmeerH/FPT-Base/resolve/main/README.md
- Command line
-
hf download hf://AmeerH/FPT-Base/README.md
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curl -L -o README.md https://huggingface.co/AmeerH/FPT-Base/resolve/main/README.md
610 Bytes
| library_name: transformers | |
| license: mit | |
| datasets: | |
| - HuggingFaceFW/fineweb-edu | |
| This is a GPT-2 model trained for 330K steps from scratch (of 1M batch size) on FineWeb-EDU i.e around 300B Tokens. | |
| ### Model Description | |
| This is the model card of a 🤗 transformers model that has been pushed on the Hub.. | |
| Developed by: Ameer H | |
| Shared by [optional]: Ameer H | |
| Model type: GPT2 | |
| Language(s) (NLP): English | |
| License: MIT | |
| ### Bias, Risks, and Limitations | |
| Will produce blabbers and unintended slurs racial or anything. Do not blame this is just an experiment. | |
| Forked from Andrej Karparthy's original model. |