Instructions to use ludolara/aya-vision-32b-americas with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ludolara/aya-vision-32b-americas with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("CohereLabs/aya-vision-32b")
model = PeftModel.from_pretrained(base_model, "ludolara/aya-vision-32b-americas")

Transformers

How to use ludolara/aya-vision-32b-americas with Transformers:

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

pipe = pipeline("text-generation", model="ludolara/aya-vision-32b-americas")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ludolara/aya-vision-32b-americas", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ludolara/aya-vision-32b-americas with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ludolara/aya-vision-32b-americas"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ludolara/aya-vision-32b-americas",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ludolara/aya-vision-32b-americas

SGLang

How to use ludolara/aya-vision-32b-americas 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 "ludolara/aya-vision-32b-americas" \
    --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": "ludolara/aya-vision-32b-americas",
		"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 "ludolara/aya-vision-32b-americas" \
        --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": "ludolara/aya-vision-32b-americas",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ludolara/aya-vision-32b-americas with Docker Model Runner:
```
docker model run hf.co/ludolara/aya-vision-32b-americas
```

Model Card for `ludolara/aya-vision-32b-americas`

ludolara/aya-vision-32b-americas is a LoRA adapter for Aya Vision 32B trained as the machine-translation stage (SFT (MT)) of the AmericasNLP 2026 system pipeline. It adapts the base VLM to generate Spanish and Indigenous-language text for Bribri, Guarani, Nahuatl, and Wixárika.

Model Details

Model Description

Developed by: Mila - Quebec AI Institute (Luis Lara, Param Raval)
Funded by [optional]: Not explicitly reported
Shared by [optional]: Luis Lara (ludolara)
Model type: PEFT LoRA adapter for a multimodal causal language model (Aya Vision 32B)
Language(s) (NLP): Spanish (es), Bribri (bzd), Guarani (grn/gn), Nahuatl (nah), Wixárika (hch)
License: cc-by-nc-4.0 (inherits base-model license and usage constraints)
Finetuned from model [optional]: CohereLabs/aya-vision-32b

Model Sources [optional]

Repository: https://github.com/ludolara/americasnlp2026
Paper [optional]: From Machine Translation to Image Captioning: Training Vision-Language Models for Indigenous Languages of the Americas (AmericasNLP 2026 system description)
Demo [optional]: Not provided

Uses

Direct Use

Spanish-to-Indigenous and Indigenous-to-Spanish machine translation candidate generation for the four overlapping AmericasNLP language tracks.
Intermediate adapter checkpoint before downstream image-captioning fine-tuning.

Downstream Use [optional]

Initialization checkpoint for SFT (IC) captioning training.
Base checkpoint for optional RLVR (MT) / GRPO refinement.

Out-of-Scope Use

Safety-critical or professional translation without expert review.
Deployment as a culturally authoritative system without community oversight.
Claims of parity with certified human translators for legal, medical, or educational contexts.

Bias, Risks, and Limitations

Training and evaluation focus on low-resource settings with limited supervised data.
Reported translation results use reference-based best-of-100 candidate selection, which is not equivalent to real interactive translation.
Outputs may contain factual errors, harmful stereotypes, or culturally inappropriate wording.
The model can underperform on dialectal variation or domains not represented in the shared-task resources.

Recommendations

Keep a human-in-the-loop, ideally with fluent speakers and community experts.
Treat reported metrics as candidate-quality signals, not production readiness.
Add task-specific moderation and output validation before any real-world deployment.

How to Get Started with the Model

Use the code below to run this adapter on top of Aya Vision 32B.

import torch
from peft import PeftModel
from transformers import AutoModelForImageTextToText, AutoProcessor

base_model_id = "CohereLabs/aya-vision-32b"
adapter_id = "ludolara/aya-vision-32b-americas"

processor = AutoProcessor.from_pretrained(base_model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    base_model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

prompt = "Traduce del español al wixárika. La casa está lejos del pueblo."
messages = [{"role": "user", "content": [{"type": "text", "text": prompt}]}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, return_tensors="pt").to(model.device)

with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=128)
print(processor.decode(out[0], skip_special_tokens=True))

Training Details

Training Data

Machine-translation stage (SFT (MT)) uses Spanish-Indigenous parallel resources for:

Bribri (bzd)
Guarani (grn)
Nahuatl (nah)
Wixárika (hch)

The system description references resources including Axolotl (Spanish-Nahuatl), Guarani-Spanish corpora, a Bribri back-translation resource, a Wixárika resource, and additional AmericasNLP shared-task materials.

Training Procedure

Preprocessing [optional]

Data is formatted as instruction-style chat prompts.
Training is bidirectional (Spanish->Indigenous and Indigenous->Spanish).
Target text is used as assistant response.

Training Hyperparameters

Training regime: LoRA SFT with bf16 + DeepSpeed ZeRO-3, early stopping enabled
Key config values (configs/lora_sft.yaml):
- epochs: 60 (early-stopped around 6.8 epochs in paper)
- learning rate: 2e-5
- warmup ratio: 0.03
- weight decay: 0.01
- per-device batch size: 32
- gradient accumulation: 1
- max sequence length: 256
- LoRA rank/alpha/dropout: 32 / 64 / 0.1

Speeds, Sizes, Times [optional]

Hardware budget in scripts targets 4 x H100 GPUs on a Slurm cluster.
End-to-end wall-clock training time is not explicitly reported.

Evaluation

Testing Data, Factors & Metrics

Testing Data

AmericasNLP MT shared-task test split (as used in the system description for overlapping languages).

Factors

Language (bzd, grn, hch, nah)
Translation direction (SPA->XXX, XXX->SPA)

Metrics

chrF++ (Popovic, 2017)
Candidate selection setting: best-of-100 with reference-based sentence-level chrF++

Results

Reported for this stage (SFT (MT)) in the paper:

Direction	BZD	GRN	HCH	NAH
SPA->XXX	24.22	29.39	27.69	26.39
XXX->SPA	33.45	38.17	30.14	31.95

Summary

This adapter establishes the translation-capable initialization used by later captioning models. It is intended as an intermediate checkpoint in the multi-stage pipeline.

Model Examination [optional]

No dedicated interpretability analysis was reported.

Environmental Impact

Carbon emissions can be estimated with the Machine Learning Impact calculator.

Hardware Type: NVIDIA H100 GPUs (script target: 4 GPUs)
Hours used: Not reported
Cloud Provider: Not reported (training scripts are Slurm/HPC-oriented)
Compute Region: Not reported
Carbon Emitted: Not reported

Technical Specifications [optional]

Model Architecture and Objective

Base architecture: Aya Vision 32B multimodal autoregressive model
Adaptation method: LoRA on attention and MLP projection modules (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
Objective: Supervised next-token training for translation prompts

Compute Infrastructure

DeepSpeed ZeRO-3 distributed setup via Slurm + Accelerate wrappers.

Hardware

Multi-GPU H100 configuration in training scripts
128 GB host memory target in scripts

Software

torch>=2.2.0
transformers>=4.46.0
datasets>=2.21.0
accelerate>=0.34.0
deepspeed>=0.16.0
trl>=0.11.0
peft 0.18.1
huggingface_hub>=0.26.0

Citation [optional]

BibTeX:

@inproceedings{lara-americasnlp-2026,
  title = "From Machine Translation to Image Captioning: Training Vision-Language Models for {I}ndigenous Languages of the {A}mericas",
  author = {Lara, Luis and
  Raval, Param},
  booktitle = "Proceedings of the Sixth Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)",
  month = jul,
  year = "2026",
  address = "San Diego, California",
  publisher = "Association for Computational Linguistics",
}

APA:

Lara, L., & Raval, P. (2026). From Machine Translation to Image Captioning: Training Vision-Language Models for Indigenous Languages of the Americas. AmericasNLP 2026.

Glossary [optional]

SFT (MT): Supervised fine-tuning on machine-translation data.
LoRA: Low-Rank Adaptation.
chrF++: Character n-gram MT metric with word-order extension.

More Information [optional]

This checkpoint is part of a staged research pipeline. For final shared-task captioning submissions, see the captioning adapters in the same namespace.

Model Card Authors [optional]

Luis Lara (maintainer), with model-card synthesis from project documentation and system paper.

Model Card Contact

luis.lara@mila.quebec

Framework versions

PEFT 0.18.1

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AmericasNLP 2026 Shared Task

Collection

AmericasNLP 2026 entry with Aya Vision 32B, using SFT for MT and SFT for Indigenous-language image captioning. • 3 items • Updated May 20

Model Card for ludolara/aya-vision-32b-americas