Instructions to use somosnlp/gemma-FULL-RAC-Colombia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use somosnlp/gemma-FULL-RAC-Colombia with Transformers:

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

pipe = pipeline("text-generation", model="somosnlp/gemma-FULL-RAC-Colombia")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("somosnlp/gemma-FULL-RAC-Colombia")
model = AutoModelForCausalLM.from_pretrained("somosnlp/gemma-FULL-RAC-Colombia")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use somosnlp/gemma-FULL-RAC-Colombia with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/somosnlp/gemma-FULL-RAC-Colombia

SGLang

How to use somosnlp/gemma-FULL-RAC-Colombia 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 "somosnlp/gemma-FULL-RAC-Colombia" \
    --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": "somosnlp/gemma-FULL-RAC-Colombia",
		"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 "somosnlp/gemma-FULL-RAC-Colombia" \
        --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": "somosnlp/gemma-FULL-RAC-Colombia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use somosnlp/gemma-FULL-RAC-Colombia with Docker Model Runner:
```
docker model run hf.co/somosnlp/gemma-FULL-RAC-Colombia
```

Update README.md

by NickyNicky - opened Mar 17, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-0

Files changed (1) hide show

README.md +9 -0

README.md CHANGED Viewed

@@ -9,6 +9,15 @@ datasets:
 - somosnlp/Reglamento_aeronautico_Colombiano_FULL_ChatML_format_gemma
 language:
 - es
 ---
 # Model Card for Gemma-FULL-RAC-Colombia 🛫

 - somosnlp/Reglamento_aeronautico_Colombiano_FULL_ChatML_format_gemma
 language:
 - es
+widget:
+- text: |
+        <bos><start_of_turn>system
+        You are a helpful AI assistant.
+        Responde en formato json.
+        Eres un agente experto en la normativa aeronautica Colombiana.<end_of_turn>
+        <start_of_turn>user
+        ¿Qué sucede con las empresas de servicios aéreos comerciales que no hayan actualizado su permiso de operación después del 31 de marzo de 2024?<end_of_turn>
+        <start_of_turn>model
 ---
 # Model Card for Gemma-FULL-RAC-Colombia 🛫