Instructions to use Rhaps360/gemma-dep-ins-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rhaps360/gemma-dep-ins-ft with Transformers:

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

pipe = pipeline("text-generation", model="Rhaps360/gemma-dep-ins-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Rhaps360/gemma-dep-ins-ft", dtype="auto")

PEFT
How to use Rhaps360/gemma-dep-ins-ft with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Rhaps360/gemma-dep-ins-ft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rhaps360/gemma-dep-ins-ft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rhaps360/gemma-dep-ins-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rhaps360/gemma-dep-ins-ft

SGLang

How to use Rhaps360/gemma-dep-ins-ft 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 "Rhaps360/gemma-dep-ins-ft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rhaps360/gemma-dep-ins-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Rhaps360/gemma-dep-ins-ft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rhaps360/gemma-dep-ins-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rhaps360/gemma-dep-ins-ft with Docker Model Runner:
```
docker model run hf.co/Rhaps360/gemma-dep-ins-ft
```

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage

from transformers import AutoTokenizer, pipeline
import torch

model = "Rhaps360/gemma-dep-ins-ft"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda" if(torch.cuda.is_available()) else "cpu",
    )

messages = [
    {"role": "user", "content": "### Context: the input message goes here. ### Response: "}
]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(
    prompt,
    max_new_tokens=300,
    do_sample=True,
    temperature=0.2,
    top_k=50,
    top_p=0.95
)
print(outputs[0]["generated_text"][len(prompt):])

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