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privacy-filter-nemotron-q8
Q8_0 quant of privacy-filter-nemotron (~1.64 GB, vs ~2.8 GB for F16) for
RAM-constrained / edge use (e.g. a 4 GB Raspberry Pi 5). The MoE expert
weights are stored 8-bit; attention, embeddings and the classifier head
stay F16. Same model, policy and runtime as the F16 entry - see
privacy-filter-nemotron for the full description.
Prefer the F16 entry when you can afford it: it is the reference artifact.
On a mixed-PII document the publisher measured q8 matching F16 on 99.93% of
token labels with an identical span set at threshold 0.5 - but one token
flipped, and for PII a single dropped span is a leak. Treat q8 as a
deliberate size/speed tradeoff and validate it on your own data.
Repository: localaiLicense: apache-2.0
ced-base-f16
CED (Consistent Ensemble Distillation, Xiaomi) is a sound-event classifier that tags everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark, ...) into the 527-class AudioSet ontology. This is the f16 GGUF for the ced backend (a standalone C++/ggml port). Recommended default: fastest on CPU and near-lossless. Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-base-q8
CED (Consistent Ensemble Distillation, Xiaomi) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). This is the q8_0 GGUF for the ced backend: smallest footprint (~88 MB, ~6.5x less memory than the PyTorch reference) and near-lossless (identical top-5 tags). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-tiny-f16
CED-tiny (5.5M params, Pi-class / edge) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). f16 GGUF for the ced backend (recommended (fastest on CPU)). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-tiny-q8
CED-tiny (5.5M params, Pi-class / edge) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). q8_0 GGUF for the ced backend (smallest footprint, near-lossless). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-mini-f16
CED-mini (9.6M params, low-power) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). f16 GGUF for the ced backend (recommended (fastest on CPU)). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-mini-q8
CED-mini (9.6M params, low-power) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). q8_0 GGUF for the ced backend (smallest footprint, near-lossless). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-small-f16
CED-small (22M params, balanced size/accuracy) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). f16 GGUF for the ced backend (recommended (fastest on CPU)). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
ced-small-q8
CED-small (22M params, balanced size/accuracy) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). q8_0 GGUF for the ced backend (smallest footprint, near-lossless). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.
Repository: localaiLicense: apache-2.0
shieldstral-1.0-3b
Shieldstral 1.0 3B is Mistral AI's compact, policy-adaptive multimodal safety
classifier. It evaluates text, images, or combined inputs against a
natural-language safety policy and answers yes or no. The model supports
twelve languages and a recommended context length of up to 32K tokens.
This entry uses the Q4_K_M GGUF quantization and includes the Pixtral vision
projector.
Repository: localaiLicense: apache-2.0
shieldstral-1.0-3b-q8
Shieldstral 1.0 3B is Mistral AI's compact, policy-adaptive multimodal safety
classifier. This higher-quality variant uses the Q8_0 GGUF quantization and
includes the Pixtral vision projector.
Repository: localaiLicense: apache-2.0
bge-m3-colbert
BAAI/bge-m3 loaded by the rerankers backend in ColBERT
(late-interaction MaxSim) mode. Pairs with the `colbert` router
classifier to score policy descriptions against the prompt
without an LLM round-trip — robust on abstract or short labels
where next-token scoring with Arch-Router-style models is noisy.
Repository: localaiLicense: mit
arch-router-1.5b-q4
Arch-Router-1.5B is a compact router LLM from Katanemo, fine-tuned from
Qwen2.5-1.5B-Instruct. Given a prompt and a set of user-defined route
policies (domain + action), it picks the best-matching policy name so
requests can be dispatched to the appropriate downstream model. Designed
for low-latency, high-throughput use inside the Arch proxy, it pairs
with LocalAI's router classifier as a preference-aligned alternative to
embedding/ColBERT-based routing on concrete, well-described policies.
Repository: localaiLicense: other
arch-router-1.5b-q8
Arch-Router-1.5B is a compact router LLM from Katanemo, fine-tuned from
Qwen2.5-1.5B-Instruct. Given a prompt and a set of user-defined route
policies (domain + action), it picks the best-matching policy name so
requests can be dispatched to the appropriate downstream model. Designed
for low-latency, high-throughput use inside the Arch proxy, it pairs
with LocalAI's router classifier as a preference-aligned alternative to
embedding/ColBERT-based routing on concrete, well-described policies.
Repository: localaiLicense: other
ideogram-4-iq4nl-ggml
Ideogram 4 is a text-to-image diffusion model known for state-of-the-art prompt adherence and exceptional, accurate text rendering inside images. It is driven by a Qwen3-VL-8B text encoder and performs real classifier-free guidance from a separate unconditional diffusion model.
This is the iQ4_NL (4-bit) quantization, a good balance of quality and footprint (~5.8GB diffusion + ~5.8GB unconditional). The bundle also pulls the Qwen3-VL-8B-Instruct text encoder and the FLUX.2 VAE. Quantized GGUF weights by stduhpf for use with stable-diffusion.cpp.
Repository: localaiLicense: ideogram-non-commercial-model-agreement
ideogram-4-q8_0-ggml
Ideogram 4 is a text-to-image diffusion model known for state-of-the-art prompt adherence and exceptional, accurate text rendering inside images. It is driven by a Qwen3-VL-8B text encoder and performs real classifier-free guidance from a separate unconditional diffusion model.
This is the Q8_0 (8-bit) quantization for highest quality (~10.1GB diffusion + ~10.1GB unconditional). The bundle also pulls the Qwen3-VL-8B-Instruct text encoder and the FLUX.2 VAE. Quantized GGUF weights by stduhpf for use with stable-diffusion.cpp.
Repository: localaiLicense: ideogram-non-commercial-model-agreement