Instructions to use alecsharpie/codegen_350m_html with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alecsharpie/codegen_350m_html with Transformers:

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

pipe = pipeline("text-generation", model="alecsharpie/codegen_350m_html")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alecsharpie/codegen_350m_html")
model = AutoModelForCausalLM.from_pretrained("alecsharpie/codegen_350m_html")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alecsharpie/codegen_350m_html with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alecsharpie/codegen_350m_html"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alecsharpie/codegen_350m_html",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alecsharpie/codegen_350m_html

SGLang

How to use alecsharpie/codegen_350m_html 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 "alecsharpie/codegen_350m_html" \
    --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": "alecsharpie/codegen_350m_html",
		"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 "alecsharpie/codegen_350m_html" \
        --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": "alecsharpie/codegen_350m_html",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alecsharpie/codegen_350m_html with Docker Model Runner:
```
docker model run hf.co/alecsharpie/codegen_350m_html
```

codegen_350m_html / README.md

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	---
	language:
	- code
	license: bsd-3-clause
	tags:
	- code
	- generative
	datasets:
	- bigcode/the-stack
	---

	# CodeGen (CodeGen-HTML 350M)

	## Model description

	CodeGen is a family of autoregressive language models for program synthesis from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`).

	The checkpoint included in this repository is finetuned on top of the CodeGen-Multi 350M, where "Multi" means the model is initialized with CodeGen-NL 350M and further pre-trained on a dataset of multiple programming languages, and "350M" refers to the number of trainable parameters.

	It has been finetuned on HTML code contained in bigcode/the-stack dataset on huggingface.

	## Training data

	This checkpoint (CodeGen-Multi 350M) was firstly initialized with CodeGen-NL 350M, and then pre-trained on [BigQuery](https://console.cloud.google.com/marketplace/details/github/github-repos), a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B tokens and includes C, C++, Go, Java, JavaScript, and Python.

	Lastly it has been finetuned on HTML code contained in [bigcode/the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset on huggingface

	## Training procedure

	Initially:
	CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
	The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism.
	See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details.

	Finetune:
	I fine tuned the 350M model on a single A100 with 40Gb of RAM, with batch size 10 and an input length of 512 tokens
	Used 80-90% of the RAM

	## Intended Use and Limitations

	As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them.
	However, the model is intended for and best at program synthesis, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.

	## How to use

	This model can be easily loaded using the `AutoModelForCausalLM` functionality:

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-multi")
	model = AutoModelForCausalLM.from_pretrained("alecsharpie/codegen_350m_html")

	text = "<body>"

	input_ids = tokenizer(text, return_tensors="pt").input_ids
	generated_ids = model.generate(input_ids, max_length=128)
	print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
	```

	## BibTeX entry and citation info

	```bibtex
	@article{Nijkamp2022ACP,
	title={A Conversational Paradigm for Program Synthesis},
	author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming},
	journal={arXiv preprint},
	year={2022}
	}
	```