Instructions to use JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA with Transformers:

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

pipe = pipeline("text-generation", model="JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA")
model = AutoModelForCausalLM.from_pretrained("JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA

SGLang

How to use JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA 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 "JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA" \
    --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": "JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA",
		"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 "JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA" \
        --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": "JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA with Docker Model Runner:
```
docker model run hf.co/JahnaviKumar/7BCodeLLama_PyCdSmry_Hetro_Central_LoRA
```

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Check out the documentation for more information.

Code Summarization without Direct Access to Code - Towards Exploring Federated LLMs for Software Engineering. The corresponding paper has been accepted in EASE-2024, for further details on the code, methodology, and results, please refer https://doi.org/10.1145/3661167.3661210. The study PEFT LoRA fine-tune an open source model Llama2 using python dataset https://github.com/EdinburghNLP/code-docstring-corpus.

datasets: - teven/code_docstring_corpus

language: - en

metrics: - bleu - meteor - rouge

tags: - llama2 - Code summary - EASE conference

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

F16