Instructions to use macadeliccc/gemma-2b-pubmed-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macadeliccc/gemma-2b-pubmed-classifier with Transformers:

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

pipe = pipeline("text-generation", model="macadeliccc/gemma-2b-pubmed-classifier")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/gemma-2b-pubmed-classifier")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/gemma-2b-pubmed-classifier", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use macadeliccc/gemma-2b-pubmed-classifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macadeliccc/gemma-2b-pubmed-classifier"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macadeliccc/gemma-2b-pubmed-classifier",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/macadeliccc/gemma-2b-pubmed-classifier

SGLang

How to use macadeliccc/gemma-2b-pubmed-classifier 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 "macadeliccc/gemma-2b-pubmed-classifier" \
    --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": "macadeliccc/gemma-2b-pubmed-classifier",
		"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 "macadeliccc/gemma-2b-pubmed-classifier" \
        --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": "macadeliccc/gemma-2b-pubmed-classifier",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use macadeliccc/gemma-2b-pubmed-classifier with Docker Model Runner:
```
docker model run hf.co/macadeliccc/gemma-2b-pubmed-classifier
```

macadeliccc commited on Jun 29, 2024

Commit

16a0f50

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@@ -3,7 +3,7 @@ base_model: google/gemma-2b
 datasets:
 - owaiskha9654/PubMed_MultiLabel_Text_Classification_Dataset_MeSH
 ---
-This lora was made for educational purposes as part of an upcoming series. The model returns a series of classifications in CSV.
 I make no claims about medical accuracy.

 datasets:
 - owaiskha9654/PubMed_MultiLabel_Text_Classification_Dataset_MeSH
 ---
+This lora was made for educational purposes as part of an upcoming series. The model returns a series of classifications in CSV. More information will be released 7/1
 I make no claims about medical accuracy.