Instructions to use neawolf/Naciro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neawolf/Naciro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="neawolf/Naciro")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("neawolf/Naciro", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use neawolf/Naciro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "neawolf/Naciro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "neawolf/Naciro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/neawolf/Naciro
- SGLang
How to use neawolf/Naciro 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 "neawolf/Naciro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "neawolf/Naciro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "neawolf/Naciro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "neawolf/Naciro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use neawolf/Naciro with Docker Model Runner:
docker model run hf.co/neawolf/Naciro
nf_distribution_schema: nf-distribution-1.0
nf_dataset: NationFiles Knowledge Data (NFKG)
nf_copyright_holder: Neawolf Media Group
nf_copyright_years: 2025–2026
nf_license_url: https://nationfiles.com/en/ai-guidelines/
nf_license_spdx: LicenseRef-NationFiles-AI-Guidelines
nf_llms_txt: https://nationfiles.com/llms.txt
nf_ai_licensing_email: ai-questions@nationfiles.com
nf_repository_relative_path: knowledge-data/md/data-integration-overview/en.md
nf_canonical_html_url: https://nationfiles.com/en/knowledge/entity/data-integration-overview/
nf_markdown_lang_file: en
Data integration at NationFiles
What we mean by it
NationFiles combines many feeds into country pages, maps, and headline figures. Data integration here is the plain-English picture: what we describe in public, and what we leave out on purpose—passwords, internal machine names, long lists of background jobs. That is normal for a live consumer site. The method pages and source registry carry the deeper detail.
Naciro
Naciro is the processing core: data is cleaned, joined, and scored along the lines set out for NFSI and in the Validation & Verification Report. Readers can judge the results from the site; they do not need backstage credentials to understand the logic in broad terms.
LPU
The LPU Architecture page explains why NationFiles uses a dedicated low-latency inference layer so busy maps and KPI refreshes stay stable instead of jumping every few seconds. It does not publish a data-centre layout or queue-by-queue runbook.
Legal notice
NationFiles legal notices mention the LPU in one sentence about live forex, crypto, and security processing. This article does not turn that into a vendor list or an internal budget sheet.
If you build on our data
Use the published schemas, honour built_at_utc when an export provides it, and link to canonical HTTPS pages. The legal sources hub and AI guidelines spell out citation and reuse.