Instructions to use Danielbrdz/Barcenas-Jev-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danielbrdz/Barcenas-Jev-0.8b with Transformers:

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

pipe = pipeline("image-text-to-text", model="Danielbrdz/Barcenas-Jev-0.8b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Danielbrdz/Barcenas-Jev-0.8b")
model = AutoModelForMultimodalLM.from_pretrained("Danielbrdz/Barcenas-Jev-0.8b", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Danielbrdz/Barcenas-Jev-0.8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danielbrdz/Barcenas-Jev-0.8b"
# 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-Jev-0.8b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Danielbrdz/Barcenas-Jev-0.8b

SGLang

How to use Danielbrdz/Barcenas-Jev-0.8b 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-Jev-0.8b" \
    --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-Jev-0.8b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-Jev-0.8b" \
        --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-Jev-0.8b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Danielbrdz/Barcenas-Jev-0.8b with Docker Model Runner:
```
docker model run hf.co/Danielbrdz/Barcenas-Jev-0.8b
```

Barcenas Jev 0.8b

Basado en el Qwen 3.5 0.8b y entrenado con el dataset Barcenas Contratos MX Decision

Es un modelo tipo Jev (Decision model) en español de México: lee un contrato y devuelve decisiones con probabilidades calibradas, no texto. Especializado en triaje contractual: qué tipo de contrato es, si requiere abogado, nivel de riesgo y urgencia.

Evaluación (test-100 sintético, antes → después)

Pregunta	acc base → ft	Brier base → ft
tipo_contrato	0.08 → 0.97	0.0593 → 0.0015
requiere_abogado	0.93 → 0.93	0.1544 → 0.0284
riesgo	0.17 → 0.73	0.0564 → 0.0319
urgencia	0.42 → 0.67	0.0529 → 0.0317

Global: accuracy 0.8325 · ECE 0.0466 · record accuracy 0.53 (las 4 bien por fila).

Barcenas Jev 0.8b

Based on Qwen 3.5 0.8b and trained on the Barcenas Contratos MX Decision dataset.

It is a "Jev" type model (decision model) in Mexican Spanish: it reads a contract and returns decisions with calibrated probabilities, rather than text. It specializes in contract triage: identifying the contract type, whether a lawyer is required, and the level of risk and urgency.

Evaluation (synthetic test-100, before → after)

Question	base acc → ft	base Brier → ft
contract_type	0.08 → 0.97	0.0593 → 0.0015
requires_lawyer	0.93 → 0.93	0.1544 → 0.0284
risk	0.17 → 0.73	0.0564 → 0.0319
urgency	0.42 → 0.67	0.0529 → 0.0317

Overall: accuracy 0.8325 · ECE 0.0466 · record accuracy 0.53 (all 4 correct per row).

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

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Safetensors

Model size

0.9B params

Tensor type

F32

BF16

Model tree for Danielbrdz/Barcenas-Jev-0.8b

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

(572)

this model

Danielbrdz
/

Barcenas-Jev-0.8b

Evaluación (test-100 sintético, antes → después)

Evaluation (synthetic test-100, before → after)

Model tree for Danielbrdz/Barcenas-Jev-0.8b

Dataset used to train Danielbrdz/Barcenas-Jev-0.8b