Instructions to use cheekymachine/email-classification-llama2-7b-peft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cheekymachine/email-classification-llama2-7b-peft with Transformers:

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

pipe = pipeline("text-generation", model="cheekymachine/email-classification-llama2-7b-peft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cheekymachine/email-classification-llama2-7b-peft")
model = AutoModelForCausalLM.from_pretrained("cheekymachine/email-classification-llama2-7b-peft")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cheekymachine/email-classification-llama2-7b-peft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cheekymachine/email-classification-llama2-7b-peft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cheekymachine/email-classification-llama2-7b-peft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cheekymachine/email-classification-llama2-7b-peft

SGLang

How to use cheekymachine/email-classification-llama2-7b-peft 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 "cheekymachine/email-classification-llama2-7b-peft" \
    --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": "cheekymachine/email-classification-llama2-7b-peft",
		"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 "cheekymachine/email-classification-llama2-7b-peft" \
        --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": "cheekymachine/email-classification-llama2-7b-peft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cheekymachine/email-classification-llama2-7b-peft with Docker Model Runner:
```
docker model run hf.co/cheekymachine/email-classification-llama2-7b-peft
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is an outcome of parameter efficient finetuning performed on Llama2-7b using the following dataset - https://huggingface.co/datasets/neelblabla/enron_labeled_email-llama2-7b_finetuning

For the workflow of fine-tuning and evaluation, please follow this GitHub link - https://github.com/neelblabla/large_language_models_for_processing_emails

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