Instructions to use SarthakBhatore/codegen-350M-mono-18k-alpaca-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SarthakBhatore/codegen-350M-mono-18k-alpaca-python with Transformers:

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

pipe = pipeline("text-generation", model="SarthakBhatore/codegen-350M-mono-18k-alpaca-python")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SarthakBhatore/codegen-350M-mono-18k-alpaca-python")
model = AutoModelForCausalLM.from_pretrained("SarthakBhatore/codegen-350M-mono-18k-alpaca-python", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SarthakBhatore/codegen-350M-mono-18k-alpaca-python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SarthakBhatore/codegen-350M-mono-18k-alpaca-python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SarthakBhatore/codegen-350M-mono-18k-alpaca-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SarthakBhatore/codegen-350M-mono-18k-alpaca-python

SGLang

How to use SarthakBhatore/codegen-350M-mono-18k-alpaca-python 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 "SarthakBhatore/codegen-350M-mono-18k-alpaca-python" \
    --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": "SarthakBhatore/codegen-350M-mono-18k-alpaca-python",
		"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 "SarthakBhatore/codegen-350M-mono-18k-alpaca-python" \
        --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": "SarthakBhatore/codegen-350M-mono-18k-alpaca-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SarthakBhatore/codegen-350M-mono-18k-alpaca-python with Docker Model Runner:
```
docker model run hf.co/SarthakBhatore/codegen-350M-mono-18k-alpaca-python
```

SarthakBhatore commited on Sep 30, 2023

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@@ -41,9 +41,3 @@ For more information on using Hugging Face models, refer to the official documen
 Fine-Tuning Details
 The CodeGen-350M-mono-18k-Alpaca-Python model was fine-tuned on the "iamtarun/python_code_instructions_18k_alpaca" dataset using the Hugging Face Transformers library. The fine-tuning process involved adapting the base Salesforce-codegen-350M model to generate Python code instructions specifically for the provided dataset.
-Regenerate


41
42	Fine-Tuning Details
43	The CodeGen-350M-mono-18k-Alpaca-Python model was fine-tuned on the "iamtarun/python_code_instructions_18k_alpaca" dataset using the Hugging Face Transformers library. The fine-tuning process involved adapting the base Salesforce-codegen-350M model to generate Python code instructions specifically for the provided dataset.