Instructions to use antflydb/GLiNER2.5-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use antflydb/GLiNER2.5-Decide with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("antflydb/GLiNER2.5-Decide") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use antflydb/GLiNER2.5-Decide with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf antflydb/GLiNER2.5-Decide:Q8_0 # Run inference directly in the terminal: llama cli -hf antflydb/GLiNER2.5-Decide:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf antflydb/GLiNER2.5-Decide:Q8_0 # Run inference directly in the terminal: llama cli -hf antflydb/GLiNER2.5-Decide:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf antflydb/GLiNER2.5-Decide:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf antflydb/GLiNER2.5-Decide:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf antflydb/GLiNER2.5-Decide:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf antflydb/GLiNER2.5-Decide:Q8_0
Use Docker
docker model run hf.co/antflydb/GLiNER2.5-Decide:Q8_0
- LM Studio
- Jan
- Ollama
How to use antflydb/GLiNER2.5-Decide with Ollama:
ollama run hf.co/antflydb/GLiNER2.5-Decide:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use antflydb/GLiNER2.5-Decide with Docker Model Runner:
docker model run hf.co/antflydb/GLiNER2.5-Decide:Q8_0
- Lemonade
How to use antflydb/GLiNER2.5-Decide with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull antflydb/GLiNER2.5-Decide:Q8_0
Run and chat with the model
lemonade run user.GLiNER2.5-Decide-Q8_0
List all available models
lemonade list
- Atomic Chat
Canonical Antfly layout (2026-10-17)
This repository follows the upstream Fastino checkpoint name. The previous
-gguf repository URL redirects here. The paired filenames identify the
Q8_0 bundle variant: the encoder matrices are Q8_0, while the auxiliary
head remains FP32 despite its Q8_0 filename suffix. The weight bytes,
tokenizer, and source configuration have not changed. The bundle descriptor
and antfly_inference_variants.json name both required components explicitly.
Historical validation reports retain the original paths and measurements;
gliner_head.gguf in those reports is now gliner2-head.Q8_0.gguf.
This DeBERTa Q8 conversion supports ordinary extraction/classification
compatibility. It does not qualify the public typed Decide endpoint:
the exporter strips decide and typed_decisions. The source checkpoint's
name and the historical browser smoke test do not confer that qualification.
Use a qualified FP32 Decide checkpoint or the separate qualified 1B Q8 bundle
for typed decisions. Native/Metal full-task and long-document qualification
must be established separately for these converted bytes.
GLiNER2.5 Decide β Antfly Q8_0 split GGUF bundle
This is an Antfly inference bundle converted from Fastino's GLiNER2.5-Decide. The encoder is Q8_0 GGUF; the GLiNER head is a separate GGUF file. The bundle uses Antfly's gliner2_split_bundle/v1 layout and is intended for Antfly's GLiNER2 inference runtime, including its browser/WASM playground. It is not a single-file llama.cpp model. Keep the files and relative paths together.
The source is Apache-2.0 licensed. The local model.safetensors used for conversion has SHA-256 40a5a23ff860dc3dff426cecd1048cacdd29c648c96db209dad818e9686dc997, matching the file in the upstream repository at revision 5a7adf72a23b4d311abae6ce050d7f0012bb3416. Model design, intended uses, limitations, and upstream evaluation are described in Fastino's model card. Fastino did not publish this Antfly conversion.
Bundle contents
antfly_inference_bundle.jsonβ split-bundle receipt naming the encoder and head.gliner2-encoder.Q8_0.ggufβ quantized encoder, SHA-2568d9bea7df21ab4a8a17333f51f246faab5280159e5751ebbadd81a85ec96c871.gliner2-head.Q8_0.ggufβ GLiNER head, SHA-25652cb6197d3dcfe5ad6276ceb037a3333d2921bbcf3dfb8289aeff82fbd5693dc.config.json,encoder_config/config.json,special_tokens_map.json,tokenizer.json,tokenizer_config.jsonβ configuration and tokenizer files from the source checkpoint.SHA256SUMSβ SHA-256 hashes for all eight bundle files.
Download the repository with hf download antflydb/GLiNER2.5-Decide --local-dir ./GLiNER2.5-Decide-Q8_0, then load that directory with an Antfly runtime that supports gliner2_split_bundle/v1. For reproducible deployments, pin a repository revision instead of using main.
Validation
Antfly's focused Decide runtime test passed 3/3 cases with these exact local bundle files and the pinned browser/WASM extraction runtime. This is a compatibility smoke test, not a full quality or cross-browser evaluation. Quantization can change outputs relative to the original checkpoint.
- Downloads last month
- 267
8-bit