Instructions to use silviasapora/gemma-7b-softplus-basic-5e-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use silviasapora/gemma-7b-softplus-basic-5e-5 with Transformers:

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

pipe = pipeline("text-generation", model="silviasapora/gemma-7b-softplus-basic-5e-5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("silviasapora/gemma-7b-softplus-basic-5e-5")
model = AutoModelForCausalLM.from_pretrained("silviasapora/gemma-7b-softplus-basic-5e-5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use silviasapora/gemma-7b-softplus-basic-5e-5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "silviasapora/gemma-7b-softplus-basic-5e-5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silviasapora/gemma-7b-softplus-basic-5e-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/silviasapora/gemma-7b-softplus-basic-5e-5

SGLang

How to use silviasapora/gemma-7b-softplus-basic-5e-5 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 "silviasapora/gemma-7b-softplus-basic-5e-5" \
    --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": "silviasapora/gemma-7b-softplus-basic-5e-5",
		"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 "silviasapora/gemma-7b-softplus-basic-5e-5" \
        --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": "silviasapora/gemma-7b-softplus-basic-5e-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use silviasapora/gemma-7b-softplus-basic-5e-5 with Docker Model Runner:
```
docker model run hf.co/silviasapora/gemma-7b-softplus-basic-5e-5
```

gemma-7b-softplus-basic-5e-5 / README.md

silviasapora

End of training

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	---
	base_model: google/gemma-7b
	datasets:
	- argilla/dpo-mix-7k
	library_name: transformers
	model_name: google/gemma-7b
	tags:
	- generated_from_trainer
	- alignment-handbook
	- trl
	- orpo
	licence: license
	---

	# Model Card for google/gemma-7b

	This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the [['argilla/dpo-mix-7k']](https://huggingface.co/datasets/['argilla/dpo-mix-7k']) dataset.
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	from transformers import pipeline

	question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
	generator = pipeline("text-generation", model="silviasapora/gemma-7b-softplus-basic-5e-5", device="cuda")
	output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
	print(output["generated_text"])
	```

	## Training procedure

	[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/silvias/huggingface/runs/xu25ngo6)


	This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).

	### Framework versions

	- TRL: 0.13.0
	- Transformers: 4.48.1
	- Pytorch: 2.5.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## Citations

	Cite ORPO as:

	```bibtex
	@article{hong2024orpo,
	title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
	author = {Jiwoo Hong and Noah Lee and James Thorne},
	year = 2024,
	eprint = {arXiv:2403.07691}
	}
	```

	Cite TRL as:

	```bibtex
	@misc{vonwerra2022trl,
	title = {{TRL: Transformer Reinforcement Learning}},
	author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year = 2020,
	journal = {GitHub repository},
	publisher = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
	}
	```