Instructions to use Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct")
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

vLLM

How to use Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct"
# 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-Nemotron-Qwen3-4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct

SGLang

How to use Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct 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-Nemotron-Qwen3-4B-Instruct" \
    --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-Nemotron-Qwen3-4B-Instruct",
		"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-Nemotron-Qwen3-4B-Instruct" \
        --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-Nemotron-Qwen3-4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct with Docker Model Runner:
```
docker model run hf.co/Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Barcenas Nemotron Qwen3 4B Instruct

Based on: Qwen/Qwen3-4B-Instruct-2507 Trained with dataset: nvidia/Nemotron-Instruction-Following-Chat-v1

The primary objective was to enhance the base model's conversational capabilities by training it on 100,000 carefully selected examples from the Nemotron dataset, which is renowned for its excellence in instruction following and structured responses.

The result is a model that delivers more coherent and better-formatted responses, with higher accuracy when following complex instructions.

Note: For me, this represents the fine-tuning process with the heaviest and most complex dataset I have undertaken to date.

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

Downloads last month: 1

Safetensors

Model size

4B params

Tensor type

F16

Model tree for Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

(1703)

this model

Quantizations

2 models

Danielbrdz
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Barcenas-Nemotron-Qwen3-4B-Instruct

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Dataset used to train Danielbrdz/Barcenas-Nemotron-Qwen3-4B-Instruct