Instructions to use k050506koch/GPT3-dev-125m-0612 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k050506koch/GPT3-dev-125m-0612 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="k050506koch/GPT3-dev-125m-0612", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("k050506koch/GPT3-dev-125m-0612", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use k050506koch/GPT3-dev-125m-0612 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf k050506koch/GPT3-dev-125m-0612 # Run inference directly in the terminal: llama cli -hf k050506koch/GPT3-dev-125m-0612
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf k050506koch/GPT3-dev-125m-0612 # Run inference directly in the terminal: llama cli -hf k050506koch/GPT3-dev-125m-0612
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf k050506koch/GPT3-dev-125m-0612 # Run inference directly in the terminal: ./llama-cli -hf k050506koch/GPT3-dev-125m-0612
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf k050506koch/GPT3-dev-125m-0612 # Run inference directly in the terminal: ./build/bin/llama-cli -hf k050506koch/GPT3-dev-125m-0612
Use Docker
docker model run hf.co/k050506koch/GPT3-dev-125m-0612
- LM Studio
- Jan
- vLLM
How to use k050506koch/GPT3-dev-125m-0612 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "k050506koch/GPT3-dev-125m-0612" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "k050506koch/GPT3-dev-125m-0612", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/k050506koch/GPT3-dev-125m-0612
- SGLang
How to use k050506koch/GPT3-dev-125m-0612 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 "k050506koch/GPT3-dev-125m-0612" \ --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": "k050506koch/GPT3-dev-125m-0612", "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 "k050506koch/GPT3-dev-125m-0612" \ --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": "k050506koch/GPT3-dev-125m-0612", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use k050506koch/GPT3-dev-125m-0612 with Ollama:
ollama run hf.co/k050506koch/GPT3-dev-125m-0612
- Unsloth Desktop
- Docker Model Runner
How to use k050506koch/GPT3-dev-125m-0612 with Docker Model Runner:
docker model run hf.co/k050506koch/GPT3-dev-125m-0612
- Lemonade
How to use k050506koch/GPT3-dev-125m-0612 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull k050506koch/GPT3-dev-125m-0612
Run and chat with the model
lemonade run user.GPT3-dev-125m-0612-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 2,524 Bytes
758903e 562fe49 4859d08 758903e 713d1c9 758903e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ---
license: mit
datasets:
- HuggingFaceFW/fineweb
language:
- en
pipeline_tag: text-generation
widget:
- text: He is a doctor. His main goal is
example_title: ' to help people.'
- text: My name is Merve and my favorite
example_title: activity is reading.
library_name: transformers
new_version: k050506koch/GPT3-dev-125m-1202
---
# GPT3
Welcome to the GPT3 repository! This project is an attempt to recreate the architecture and approach from the original OpenAI GPT-3 paper. The repository includes scripts for training, fine-tuning, and inference of a GPT-3-like model using PyTorch and the Hugging Face Transformers library.
Here are located weights of dev checkpoints of my models. You can always download a folder, paste it's path inside inference.py and chat with them.
# **You can find all code on [GitHub](https://github.com/krll-corp/GPT3)**
# Note: This is a model with 125 million parameters. It was trained on 3.6Bn tokens. (Of course, it's very undertrained, but this one should be a technology demonstrator.)
# Note 2: This is a model checkpoint released on 06/12 2024 and has been trained for longer (12 batch size, 4 grad accumulation, 512 tokens and 600,000 steps). It scores 27.65% on MMLU which is slightly higher than 25% (random guess)
## inference:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('k050506koch/GPT3-dev-125m-0612', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('k050506koch/GPT3-dev-125m-0612')
tokenizer.pad_token_id = tokenizer.eos_token_id
print("\n", tokenizer.decode(model.generate(tokenizer.encode("He is a doctor. His main goal is", return_tensors='pt'),
max_length=128, temperature=0.7, top_p=0.9, repetition_penalty=1.2, no_repeat_ngram_size=3,
num_return_sequences=1, do_sample=True)[0], skip_special_tokens=True))
```
## Contributing
Contributions are welcome! I'm just a student who is interested in AI so my code may be incorrect or have logical issues. Please open an issue or submit a pull request for any improvements or bug fixes, I will be happy.
## License
This project is licensed under the MIT License. See the LICENSE file for details. Everyone can use and modify this code at their discretion.
## Acknowledgements
Thanks OpenAI, HuggingFace and Pytorch for making this project possible!
- [OpenAI GPT-3 Paper](https://arxiv.org/abs/2005.14165)
- [Hugging Face Transformers](https://github.com/huggingface/transformers)
- [PyTorch](https://pytorch.org/) |