Instructions to use not-lain/PyGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use not-lain/PyGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="not-lain/PyGPT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("not-lain/PyGPT") model = AutoModelForCausalLM.from_pretrained("not-lain/PyGPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use not-lain/PyGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "not-lain/PyGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "not-lain/PyGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/not-lain/PyGPT
- SGLang
How to use not-lain/PyGPT 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 "not-lain/PyGPT" \ --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": "not-lain/PyGPT", "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 "not-lain/PyGPT" \ --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": "not-lain/PyGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use not-lain/PyGPT with Docker Model Runner:
docker model run hf.co/not-lain/PyGPT
File size: 1,616 Bytes
89fc89a b232876 5c49efd b232876 | 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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | ---
datasets:
- iamtarun/python_code_instructions_18k_alpaca
widget:
- text: >
Below is an instruction that describes a task. Write a response that
appropriately completes the request.
### Instruction:
Create a function to calculate the sum of a sequence of integers.
### Input:
[1, 2, 3, 4, 5]
### Output:
pipeline_tag: text-generation
tags:
- code
---
## Model Details
this is the finetuned version of GPT2 on a coding dataset
### Model Description
- **Model type:** text-generation
- **Finetuned from model [GPT2](https://huggingface.co/gpt2)**
### Model Sources
- **Repository:** https://huggingface.co/gpt2
## Uses
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="not-lain/PyGPT")
prompt = """
Below is an instruction that describes a task. Write a response that
appropriately completes the request.
### Instruction:
Create a function to calculate the sum of a sequence of integers.
### Input:
[1, 2, 3, 4, 5]
### Output:
"""
pipe(prompt)
```
## Bias, Risks, and Limitations
model may produce biased ,erroneous and output.
### Recommendations
it is not advised to use this model as it is just a product of testing a finetuning script
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Evaluation
please refer to the tensorboard tab for full details |