Instructions to use Nan-Do/python-assistant-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nan-Do/python-assistant-3b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Nan-Do/python-assistant-3b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nan-Do/python-assistant-3b")
model = AutoModelForCausalLM.from_pretrained("Nan-Do/python-assistant-3b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nan-Do/python-assistant-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nan-Do/python-assistant-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nan-Do/python-assistant-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nan-Do/python-assistant-3b

SGLang

How to use Nan-Do/python-assistant-3b 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 "Nan-Do/python-assistant-3b" \
    --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": "Nan-Do/python-assistant-3b",
		"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 "Nan-Do/python-assistant-3b" \
        --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": "Nan-Do/python-assistant-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nan-Do/python-assistant-3b with Docker Model Runner:
```
docker model run hf.co/Nan-Do/python-assistant-3b
```

Nan-Do commited on Aug 4, 2023

Commit

4e74f0c

1 Parent(s): e739fd0

Updated formating

Browse files

Files changed (1) hide show

README.md +2 -4

README.md CHANGED Viewed

@@ -53,8 +53,7 @@ while running:
 ```
 Next there are a few samples to show how the model understands the code.
-In this first example the model is able to answer correctly what shape is being drawn and even tells the position correctly,
-it gets the color wrong in this case
 ```
 Below is an instruction that describes a task. Write a response that appropriately completes the request.
@@ -126,8 +125,7 @@ if __name__ == '__main__':
 '''
 ### Response:
-You can use `pygame.time.Clock` to control the frames per second and limit the number of frames to prevent
-your program from freezing. Here's a modified version of your code with this addition:
 '''python
 import pygame

 ```
 Next there are a few samples to show how the model understands the code.
+In this first example the model is able to answer correctly what shape is being drawn and even tells the position correctly, it gets the color wrong in this case
 ```
 Below is an instruction that describes a task. Write a response that appropriately completes the request.
 '''
 ### Response:
+You can use `pygame.time.Clock` to control the frames per second and limit the number of frames to prevent your program from freezing. Here's a modified version of your code with this addition:
 '''python
 import pygame