Instructions to use Danielbrdz/Barcenas-Tiny-1.1b-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danielbrdz/Barcenas-Tiny-1.1b-DPO with Transformers:

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

pipe = pipeline("text-generation", model="Danielbrdz/Barcenas-Tiny-1.1b-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Danielbrdz/Barcenas-Tiny-1.1b-DPO")
model = AutoModelForCausalLM.from_pretrained("Danielbrdz/Barcenas-Tiny-1.1b-DPO", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Danielbrdz/Barcenas-Tiny-1.1b-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danielbrdz/Barcenas-Tiny-1.1b-DPO"
# 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-Tiny-1.1b-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Danielbrdz/Barcenas-Tiny-1.1b-DPO

SGLang

How to use Danielbrdz/Barcenas-Tiny-1.1b-DPO 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-Tiny-1.1b-DPO" \
    --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-Tiny-1.1b-DPO",
		"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-Tiny-1.1b-DPO" \
        --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-Tiny-1.1b-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Danielbrdz/Barcenas-Tiny-1.1b-DPO with Docker Model Runner:
```
docker model run hf.co/Danielbrdz/Barcenas-Tiny-1.1b-DPO
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Barcenas Tiny 1.1b DPO

It is a model based on the famous TinyLlama/TinyLlama-1.1B-Chat-v1.0 and trained with DPO using the Intel/orca_dpo_pairs dataset.

With its reinforcement based training we hope to improve the Tiny model in a huge way and have a better model with better responses with a small size and accessible to most people.

Many thanks to Maxime Labonne (mlabonne) for his tutorial on how to train a LLM model using DPO, without his tutorial this model would not have been possible.

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

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Safetensors

Model size

1B params

Tensor type

F16

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Finetunes

1 model

Quantizations

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Danielbrdz
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Barcenas-Tiny-1.1b-DPO

Model tree for Danielbrdz/Barcenas-Tiny-1.1b-DPO

Dataset used to train Danielbrdz/Barcenas-Tiny-1.1b-DPO