Instructions to use Danielbrdz/Barcenas-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danielbrdz/Barcenas-9b with Transformers:

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

pipe = pipeline("text-generation", model="Danielbrdz/Barcenas-9b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Danielbrdz/Barcenas-9b")
model = AutoModelForCausalLM.from_pretrained("Danielbrdz/Barcenas-9b")
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 Danielbrdz/Barcenas-9b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Danielbrdz/Barcenas-9b

SGLang

How to use Danielbrdz/Barcenas-9b 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 "Danielbrdz/Barcenas-9b" \
    --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": "Danielbrdz/Barcenas-9b",
		"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 "Danielbrdz/Barcenas-9b" \
        --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": "Danielbrdz/Barcenas-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Danielbrdz/Barcenas-9b with Docker Model Runner:
```
docker model run hf.co/Danielbrdz/Barcenas-9b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Barcenas 9B

Barcenas 9B is a powerful language model based on 01-ai/Yi-1.5-9B-Chat and fine-tuned with data from yahma/alpaca-cleaned. This AI model is designed to provide coherent and detailed responses for natural language processing (NLP) tasks.

Key Features

Model Size: With 9 billion parameters, Barcenas 9B can handle complex tasks and deliver high-quality responses. Model Base: Derived from the 01-ai/Yi-1.5-9B-Chat model, known for its ability to maintain fluid and natural conversations. Additional Training: Fine-tuned with data from yahma/alpaca-cleaned, enhancing its ability to understand and generate natural language accurately.

Applications

Barcenas 9B is ideal for a wide range of applications, including but not limited to:

Virtual Assistants: Provides quick and accurate responses in customer service and personal assistant systems. Content Generation: Useful for creating articles, blogs, and other written content. Sentiment Analysis: Capable of interpreting and analyzing emotions in texts, aiding in market research and social media analysis. Machine Translation: Facilitates text translation with high accuracy and contextual coherence.

Training and Fine-Tuning The model was initially trained using the robust and versatile 01-ai/Yi-1.5-9B-Chat, known for its performance in conversational tasks. It was then fine-tuned with the clean and curated data from yahma/alpaca-cleaned, significantly enhancing its ability to understand and generate more natural and contextually appropriate responses.

Benefits High Performance: With a large number of parameters and high-quality training data, Barcenas 9B offers exceptional performance in NLP tasks. Versatility: Adaptable to multiple domains and applications, from customer service to creative content generation. Improved Accuracy: Fine-tuning with specific data ensures higher accuracy and relevance in the generated responses.

Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽

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Safetensors

Model size

9B params

Tensor type

F16

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Quantizations

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Dataset used to train Danielbrdz/Barcenas-9b