Instructions to use GuillermoBarrio/gemma-2-9b-electoral-2019 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GuillermoBarrio/gemma-2-9b-electoral-2019 with Transformers:

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

pipe = pipeline("text-generation", model="GuillermoBarrio/gemma-2-9b-electoral-2019")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GuillermoBarrio/gemma-2-9b-electoral-2019")
model = AutoModelForCausalLM.from_pretrained("GuillermoBarrio/gemma-2-9b-electoral-2019", device_map="auto")
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 Settings

vLLM

How to use GuillermoBarrio/gemma-2-9b-electoral-2019 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GuillermoBarrio/gemma-2-9b-electoral-2019"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GuillermoBarrio/gemma-2-9b-electoral-2019",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GuillermoBarrio/gemma-2-9b-electoral-2019

SGLang

How to use GuillermoBarrio/gemma-2-9b-electoral-2019 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 "GuillermoBarrio/gemma-2-9b-electoral-2019" \
    --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": "GuillermoBarrio/gemma-2-9b-electoral-2019",
		"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 "GuillermoBarrio/gemma-2-9b-electoral-2019" \
        --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": "GuillermoBarrio/gemma-2-9b-electoral-2019",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use GuillermoBarrio/gemma-2-9b-electoral-2019 with Docker Model Runner:
```
docker model run hf.co/GuillermoBarrio/gemma-2-9b-electoral-2019
```

Model Card for Model ID

This is a fine-tuned Gemma-2-9b model. Fine tuning was carried out using a synthetic dataset of 1,800 questions and answers in Spanish language divided in three levels of complexity. The synthetic dataset was generated using Python and, the highest level of complexity, I used the V1 LLM of DeepSeek. The original dataset included the complete electoral results of both 2019 general elections (April and November), along with some socio-economic data. The electoral results were collected at 'section level', around an average of 700-800 electors. There are around 36,500 sections in Spain, therefore the dataset had 72,500 rows and 95 columns.

I finetuned the model using the transformer library, obtaining a Validation Loss of 0.32 after 3 epochs.

Model Details

Model Description

The model answers questions about the election results, and their relations with socioeconomic data, such as income per capita.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Guillermo Barrio [More Information Needed]
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Uses

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Recommendations

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How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Safetensors

Model size

9B params

Tensor type

F32

BF16

Model tree for GuillermoBarrio/gemma-2-9b-electoral-2019

Base model

google/gemma-2-9b

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Paper for GuillermoBarrio/gemma-2-9b-electoral-2019

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61