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bge-large-en-v1.5-f16
BGE-large-en-v1.5 is a 335M English embedding model for semantic search
and retrieval. This F16 GGUF build produces 1024-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-large-en-v1.5-q8-0
BGE-large-en-v1.5 is a 335M English embedding model for semantic search
and retrieval. This Q8_0 GGUF build produces 1024-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-large-en-v1.5-q4-k-m
BGE-large-en-v1.5 is a 335M English embedding model for semantic search
and retrieval. This Q4_K_M GGUF build produces 1024-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-large-en-v1.5-transformers
BGE-large-en-v1.5 is a 335M English embedding model for semantic search
and retrieval. This safetensors build uses the Transformers backend's
SentenceTransformer loader, producing 1024-dimensional embeddings with
a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-base-en-v1.5-f16
BGE-base-en-v1.5 is a 109M English embedding model for semantic search
and retrieval. This F16 GGUF build produces 768-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-base-en-v1.5-q8-0
BGE-base-en-v1.5 is a 109M English embedding model for semantic search
and retrieval. This Q8_0 GGUF build produces 768-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-base-en-v1.5-q4-k-m
BGE-base-en-v1.5 is a 109M English embedding model for semantic search
and retrieval. This Q4_K_M GGUF build produces 768-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-small-en-v1.5-f16
BGE-small-en-v1.5 is a 33M English embedding model for semantic search
and retrieval. This F16 GGUF build produces 384-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-small-en-v1.5-q8-0
BGE-small-en-v1.5 is a 33M English embedding model for semantic search
and retrieval. This Q8_0 GGUF build produces 384-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-small-en-v1.5-q4-k-m
BGE-small-en-v1.5 is a 33M English embedding model for semantic search
and retrieval. This Q4_K_M GGUF build produces 384-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
ornith-1.5-9b-uncensored
# Ornith-1.5-9B-uncensored
An **abliterated** (refusal-direction-ablated) build of
`ornith-ai/Ornith-1.5-9B`, produced with
ZeroFuse and published by
junafinity.
This is the **9B control checkpoint** (bf16). Mac users should start from the MLX-8bit or GGUF-8bit siblings. The official 9B base has **no `mtp.*` tensors**; nothing was grafted.
**Vision tower and MTP heads are preserved** — see
Vision & MTP preservation for the before/after audit.
## Intended use: red teaming and defensive cybersecurity research
These uncensored (abliterated) weights are built as a **research instrument** for red teaming and defensive cybersecurity work. Safety training suppresses the *display* of capability, not capability itself. A refusal tells you the model declined. It does not tell you whether the weights could have complied. That conflation underestimates the true ceiling and hides holes in *your* filters, classifiers, and policy layer.
Use each uncensored checkpoint as the **treatment half of a controlled pair** against its original base model:
...
Repository: localaiLicense: apache-2.0
apodex-1.1-mini-q4
Apodex-1.1-mini is an Apache-2.0 Qwen3.5 mixture-of-experts model for
long-horizon research, data analysis, coding, file work, and tool use. It
activates about 3B of its 35.95B parameters per token and supports text and
image input with a context window of 262K tokens.
This default entry uses the recommended Q4_K_M GGUF and F16 vision
projector. An MTP-enabled build and a higher-quality Q8_0 model are
available as variants.
Repository: localaiLicense: apache-2.0
Attention: Trust Remote Code is required for this model
wemm-embedding-2b
WeMM-Embedding-2B is Tencent's Apache-2.0 multilingual embedding model
built on Qwen3.5. This entry serves the original bfloat16 safetensors with
LocalAI's Transformers backend and produces 2,048-dimensional normalized
embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
Attention: Trust Remote Code is required for this model
wemm-embedding-4b
WeMM-Embedding-4B is Tencent's mid-sized Apache-2.0 multilingual embedding
model built on Qwen3.5. This entry serves the original bfloat16 safetensors
with LocalAI's Transformers backend and produces 2,560-dimensional
normalized embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
Attention: Trust Remote Code is required for this model
wemm-embedding-9b
WeMM-Embedding-9B is Tencent's largest Apache-2.0 multilingual embedding
model built on Qwen3.5. This entry serves the original bfloat16 safetensors
with LocalAI's Transformers backend and produces 4,096-dimensional
normalized embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
ling-3.0-flash-iq1
Ling-3.0-flash is InclusionAI's MIT-licensed hybrid reasoning MoE model
with 124B total parameters and 5.5B active parameters per token. It targets
coding, deep research, instruction following, and agentic workflows with a
native 256K-token context window.
This default entry uses the 36.5 GB AD-IQ1_M GGUF. A higher-quality
44.7 GB AD-IQ2_XS model is available as a variant.
Repository: localaiLicense: mit
ornith-1.5-9b-uncensored-q4
Junafinity's Ornith-1.5-9B Uncensored is a 9B-parameter Qwen3.5
derivative with refusal-direction ablation for research and evaluation.
This Q4_K_M GGUF build includes the F16 vision projector and uses
the embedded chat template for text and image conversations.
Repository: localaiLicense: apache-2.0
ornith-1.5-9b-uncensored-q6
Junafinity's Ornith-1.5-9B Uncensored is a 9B-parameter Qwen3.5
derivative with refusal-direction ablation for research and evaluation.
This Q6_K GGUF build includes the F16 vision projector and uses
the embedded chat template for text and image conversations.
Repository: localaiLicense: apache-2.0
ornith-1.5-9b-uncensored-q8
Junafinity's Ornith-1.5-9B Uncensored is a 9B-parameter Qwen3.5
derivative with refusal-direction ablation for research and evaluation.
This Q8_0 GGUF build includes the F16 vision projector and uses
the embedded chat template for text and image conversations.
Repository: localaiLicense: apache-2.0
ornith-1.5-9b-obliterated-q4
Ornith-1.5-9B OBLITERATED is a refusal-removed derivative for alignment
research, red teaming, coding, reasoning, and agentic tasks. Its safety
guardrails are removed, and the publisher reports some capability loss
compared with the original model.
This default entry uses the Q4_K_M GGUF and BF16 vision projector. The
linked variant uses the higher-quality Q8_0 quantization.
Repository: localaiLicense: mit
thomson-1.0-small-q4
Thomson-1.0-Small is a 35B-parameter mixture-of-experts model with about
3B active parameters. It focuses on legal, tax, journalism, research,
reasoning, tool use, and document processing. It supports text and image
input with a native context window of 262K tokens.
This default entry uses the Q4_K_M GGUF and BF16 vision projector. A
higher-quality Q8_0 model is available as a variant.
Repository: localaiLicense: polyform-strict-1.0.0
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