Instructions to use iamtarun/pycompetitive-codegen350M-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iamtarun/pycompetitive-codegen350M-qlora with Transformers:

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

pipe = pipeline("text-generation", model="iamtarun/pycompetitive-codegen350M-qlora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iamtarun/pycompetitive-codegen350M-qlora")
model = AutoModelForCausalLM.from_pretrained("iamtarun/pycompetitive-codegen350M-qlora")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use iamtarun/pycompetitive-codegen350M-qlora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamtarun/pycompetitive-codegen350M-qlora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamtarun/pycompetitive-codegen350M-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/iamtarun/pycompetitive-codegen350M-qlora

SGLang

How to use iamtarun/pycompetitive-codegen350M-qlora 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 "iamtarun/pycompetitive-codegen350M-qlora" \
    --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": "iamtarun/pycompetitive-codegen350M-qlora",
		"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 "iamtarun/pycompetitive-codegen350M-qlora" \
        --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": "iamtarun/pycompetitive-codegen350M-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use iamtarun/pycompetitive-codegen350M-qlora with Docker Model Runner:
```
docker model run hf.co/iamtarun/pycompetitive-codegen350M-qlora
```

iamtarun commited on Jul 28, 2023

Commit

f5ea8d4

1 Parent(s): 43ab4b7

Update README.md

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README.md CHANGED Viewed

@@ -56,8 +56,8 @@ def generate_prompt(description, inputs, outputs):
 from transformers import AutoModelForCausalLM, AutoTokenizer
 # load model and tokenizer
-model = AutoModelForCausalLM.from_pretrained(args.model_path, device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained(args.model_path)
 # loading model for inference
 model.eval()

 from transformers import AutoModelForCausalLM, AutoTokenizer
 # load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("iamtarun/pycompetitive-codegen350M-qlora", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("iamtarun/pycompetitive-codegen350M-qlora")
 # loading model for inference
 model.eval()