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

Libraries

How to use mrudulajethe/reddit_binary_classification_gemma with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b")
model = PeftModel.from_pretrained(base_model, "mrudulajethe/reddit_binary_classification_gemma")

Transformers

How to use mrudulajethe/reddit_binary_classification_gemma with Transformers:

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

pipe = pipeline("text-generation", model="mrudulajethe/reddit_binary_classification_gemma")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mrudulajethe/reddit_binary_classification_gemma", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrudulajethe/reddit_binary_classification_gemma with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mrudulajethe/reddit_binary_classification_gemma

SGLang

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

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

reddit_binary_classification_gemma / training_args.bin

mrudulajethe

Upload folder using huggingface_hub

b8285df verified 11 months ago

History Blame Contribute Delete

6.35 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: a3a211c957a4b59cbe1e501b866a7ea4e0e884e5546d3d671729a984a5439941
Size of remote file:: 6.35 kB
SHA256:: 686ebe79d27200612ac62728e89a5bb8a8c25cee7ec88b07fd83200fd78ba227

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

mrudulajethe
/

reddit_binary_classification_gemma

Detected Pickle imports (10)

Xet Pointer Details