Instructions to use gia-uh/cecilia-2b-instruct-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gia-uh/cecilia-2b-instruct-v1 with Transformers:

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

pipe = pipeline("text-generation", model="gia-uh/cecilia-2b-instruct-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gia-uh/cecilia-2b-instruct-v1")
model = AutoModelForCausalLM.from_pretrained("gia-uh/cecilia-2b-instruct-v1")
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]:]))

llama-cpp-python

How to use gia-uh/cecilia-2b-instruct-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="gia-uh/cecilia-2b-instruct-v1",
	filename="cecilia-2b-instruct-v1.f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use gia-uh/cecilia-2b-instruct-v1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gia-uh/cecilia-2b-instruct-v1:F16
# Run inference directly in the terminal:
llama cli -hf gia-uh/cecilia-2b-instruct-v1:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gia-uh/cecilia-2b-instruct-v1:F16
# Run inference directly in the terminal:
llama cli -hf gia-uh/cecilia-2b-instruct-v1:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf gia-uh/cecilia-2b-instruct-v1:F16
# Run inference directly in the terminal:
./llama-cli -hf gia-uh/cecilia-2b-instruct-v1:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf gia-uh/cecilia-2b-instruct-v1:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gia-uh/cecilia-2b-instruct-v1:F16

Use Docker

docker model run hf.co/gia-uh/cecilia-2b-instruct-v1:F16

LM Studio
Jan

vLLM

How to use gia-uh/cecilia-2b-instruct-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gia-uh/cecilia-2b-instruct-v1:F16

SGLang

How to use gia-uh/cecilia-2b-instruct-v1 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 "gia-uh/cecilia-2b-instruct-v1" \
    --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": "gia-uh/cecilia-2b-instruct-v1",
		"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 "gia-uh/cecilia-2b-instruct-v1" \
        --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": "gia-uh/cecilia-2b-instruct-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use gia-uh/cecilia-2b-instruct-v1 with Ollama:
```
ollama run hf.co/gia-uh/cecilia-2b-instruct-v1:F16
```

Unsloth Studio

How to use gia-uh/cecilia-2b-instruct-v1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for gia-uh/cecilia-2b-instruct-v1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for gia-uh/cecilia-2b-instruct-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for gia-uh/cecilia-2b-instruct-v1 to start chatting

Atomic Chat new
Docker Model Runner
How to use gia-uh/cecilia-2b-instruct-v1 with Docker Model Runner:
```
docker model run hf.co/gia-uh/cecilia-2b-instruct-v1:F16
```

Lemonade

How to use gia-uh/cecilia-2b-instruct-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gia-uh/cecilia-2b-instruct-v1:F16

Run and chat with the model

lemonade run user.cecilia-2b-instruct-v1-F16

List all available models

lemonade list

Cecilia: The Cuban Language Model

Cecilia is a family of language models continual pretrained specifically on Cuban written text, capturing the linguistic, cultural, and social nuances of Cuban Spanish. These models are designed to support natural language processing tasks with a focus on Cuban language varieties and cultural context.

About Cecilia FT MS v1

This model is a fine-tuned version of Cecilia 2B v0.1 which is a continual pre-trained model based on Salamandra 2b. It belongs to the Cecilia collection and follows the same lineage as Cecilia 2B v0.1.

Model Formats

This repository is a Hybrid Release containing:

Safetensors: For use with Hugging Face transformers.
GGUF (FP16): For use with llama.cpp, vLLM, or local inference tools.

Quantizations

Official quantized GGUF versions (Q8_0, Q6_K, Q4_K_M) in the repository gia-uh/cecilia-2b-instruct-v1-GGUF

Quickstart (Transformers)

from transformers import AutoConfig, AutoModel, AutoTokenizer

repo_id = "gia-uh/cecilia_ft_ms_v1"

# Load model and tokenizer
config = AutoConfig.from_pretrained(repo_id, trust_remote_code=False)
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModel.from_pretrained(repo_id, trust_remote_code=False)

# Simple inference
inputs = tokenizer("Hola, que bolá?", return_tensors="pt")
outputs = model(**inputs)