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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

llm-jp-4-33b-thinking-bf16
LLM-jp-4-33B-thinking in the official 66.4 GB BF16 GGUF format. This variant preserves the original model precision for hosts with enough memory.

Repository: localaiLicense: apache-2.0

neohorse-1-4b-bf16
NeoHorse-1-4B is TokenRhythm's text-only Qwen3.5-4B fine-tune for coding, reasoning, and agentic tasks. This official BF16 GGUF build uses the embedded Jinja chat template and a 32K-token default context.

Repository: localaiLicense: apache-2.0

qwen3-coder-30b-a3b-vllm-cpp
Qwen3-Coder-30B-A3B on vllm.cpp: a coding and agentic-tool-use model, 30B total parameters with about 3B active per token, gated token-exact against vLLM on this engine. The tool-call parser is named explicitly rather than auto-detected, and that matters here. Qwen3-Coder's tool dialect is byte-identical on the wire to another family's, so template sniffing cannot separate the two and would fall back to the wrong parser. With qwen3_coder named, tool calls arrive as real tool_calls on the OpenAI response. This is the bf16 checkpoint, roughly 57 GB of weights, which is what the engine was gated on. Being bf16 rather than NVFP4 it does not need Blackwell on its own account, but LocalAI's CUDA images for this backend are currently built for Blackwell-family GPUs only, so on an older card use the CPU build.

Repository: localaiLicense: apache-2.0

qwen3-4b-vllm-cpp
Qwen3-4B on vllm.cpp, in bf16. The small end of the engine's gated dense family, which reaches parity with vLLM on every axis at concurrency 1. bf16 rather than NVFP4 on purpose: this is the entry that runs where the flagship NVFP4 checkpoints cannot, including Apple Silicon via Metal, Vulkan and plain CPU. Roughly 8 GB of weights, plus about 4.5 GB of KV cache at the context configured here. Tool calling and the thinking split are parsed inside the engine.

Repository: localaiLicense: apache-2.0

qwen3-0.6b-vllm-cpp
Qwen3-0.6B on vllm.cpp, in bf16. Roughly 1.4 GB of weights, which makes it the cheapest way to confirm a vllm-cpp install actually serves before committing disk and memory to one of the large checkpoints. It runs anywhere the backend does, CPU included, and it is a real chat model rather than a stub, so tool calling and the thinking split can be exercised on it too.

Repository: localaiLicense: apache-2.0

allenai_olmo-3.1-32b-think
The **Olmo-3.1-32B-Think** model is a large language model (LLM) optimized for efficient inference using quantized versions. It is a quantized version of the original **allenai/Olmo-3.1-32B-Think** model, developed by **bartowski** using the **imatrix** quantization method. ### Key Features: - **Base Model**: `allenai/Olmo-3.1-32B-Think` (unquantized version). - **Quantized Versions**: Available in multiple formats (e.g., `Q6_K_L`, `Q4_1`, `bf16`) with varying precision (e.g., Q8_0, Q6_K_L, Q5_K_M). These are derived from the original model using the **imatrix calibration dataset**. - **Performance**: Optimized for low-memory usage and efficient inference on GPUs/CPUs. Recommended quantization types include `Q6_K_L` (near-perfect quality) or `Q4_K_M` (default, balanced performance). - **Downloads**: Available via Hugging Face CLI. Split into multiple files if needed for large models. - **License**: Apache-2.0. ### Recommended Quantization: - Use `Q6_K_L` for highest quality (near-perfect performance). - Use `Q4_K_M` for balanced performance and size. - Avoid lower-quality options (e.g., `Q3_K_S`) unless specific hardware constraints apply. This model is ideal for deploying on GPUs/CPUs with limited memory, leveraging efficient quantization for practical use cases.

Repository: localaiLicense: apache-2.0

hunyuan-ocr-bf16
HunyuanOCR in BF16 GGUF format for maximum model and vision-projector fidelity. It runs on llama.cpp and supports document parsing, text spotting, information extraction, and text-image translation.

Repository: localaiLicense: tencent-hunyuan-community

huihui-ai_huihui-gpt-oss-20b-bf16-abliterated
This is an uncensored version of unsloth/gpt-oss-20b-BF16 created with abliteration (see remove-refusals-with-transformers to know more about it).

Repository: localaiLicense: apache-2.0

kimodo-soma-rp-bf16
Kimodo SOMA RP v1.1 text-to-motion on CPU or Vulkan, with F32 motion weights and the shared monolithic BF16 Llama-3/LLM2Vec text encoder. Exports an animated skeleton GLB without a mesh or skin. Motion and text weights retain their respective NVIDIA Open Model License and Llama 3 terms. BF16 needs substantial memory with all layers resident; use text_layer_chunk:8 on smaller GPUs.

Repository: localai

kimodo-soma-seed-bf16
Kimodo SOMA SEED v1.1 text-to-motion on CPU or Vulkan, with F32 motion weights and the shared monolithic BF16 Llama-3/LLM2Vec text encoder. Exports an animated skeleton GLB without a mesh or skin. Motion and text weights retain their respective NVIDIA Open Model License and Llama 3 terms. BF16 needs substantial memory with all layers resident; use text_layer_chunk:8 on smaller GPUs.

Repository: localai

kimodo-g1-rp-bf16
Kimodo G1 RP v1 text-to-motion on CPU or Vulkan, with F32 motion weights and the shared monolithic BF16 Llama-3/LLM2Vec text encoder. Exports an animated skeleton GLB without a mesh or skin. Motion and text weights retain their respective NVIDIA Open Model License and Llama 3 terms. BF16 needs substantial memory with all layers resident; use text_layer_chunk:8 on smaller GPUs.

Repository: localai

kimodo-g1-seed-bf16
Kimodo G1 SEED v1 text-to-motion on CPU or Vulkan, with F32 motion weights and the shared monolithic BF16 Llama-3/LLM2Vec text encoder. Exports an animated skeleton GLB without a mesh or skin. Motion and text weights retain their respective NVIDIA Open Model License and Llama 3 terms. BF16 needs substantial memory with all layers resident; use text_layer_chunk:8 on smaller GPUs.

Repository: localai

gpt-oss-20b-esper3.1-i1
**Model Name:** gpt-oss-20b-Esper3.1 **Repository:** [ValiantLabs/gpt-oss-20b-Esper3.1](https://huggingface.co/ValiantLabs/gpt-oss-20b-Esper3.1) **Base Model:** openai/gpt-oss-20b **Type:** Instruction-tuned, reasoning-focused language model **Size:** 20 billion parameters **License:** Apache 2.0 --- ### 🔍 **Overview** gpt-oss-20b-Esper3.1 is a specialized, instruction-tuned variant of the 20B open-source GPT model, developed by **Valiant Labs**. It excels in **advanced coding, software architecture, and DevOps reasoning**, making it ideal for technical problem-solving and AI-driven engineering tasks. ### ✨ **Key Features** - **Expert in DevOps & Cloud Systems:** Trained on high-difficulty datasets (e.g., Titanium3, Tachibana3, Mitakihara), it delivers precise, actionable guidance for AWS, Kubernetes, Terraform, Ansible, Docker, Jenkins, and more. - **Strong Code Reasoning:** Optimized for complex programming tasks, including full-stack development, scripting, and debugging. - **High-Quality Inference:** Uses `bf16` precision for full-precision performance; quantized versions (e.g., GGUF) available for efficient local inference. - **Open-Source & Free to Use:** Fully open-access, built on the public gpt-oss-20b foundation and trained with community datasets. ### 📌 **Use Cases** - Designing scalable cloud architectures - Writing and optimizing infrastructure-as-code - Debugging complex DevOps pipelines - AI-assisted software development and documentation - Real-time technical troubleshooting ### 💡 **Getting Started** Use the standard `text-generation` pipeline with the `transformers` library. Supports role-based prompting (e.g., `user`, `assistant`) and performs best with high-reasoning prompts. ```python from transformers import pipeline pipe = pipeline("text-generation", model="ValiantLabs/gpt-oss-20b-Esper3.1", torch_dtype="auto", device_map="auto") messages = [{"role": "user", "content": "Design a Kubernetes cluster for a high-traffic web app with CI/CD via GitHub Actions."}] outputs = pipe(messages, max_new_tokens=2000) print(outputs[0]["generated_text"][-1]) ``` --- > 🔗 **Model Gallery Entry**: > *gpt-oss-20b-Esper3.1 – A powerful, open-source 20B model tuned for expert-level DevOps, coding, and system architecture. Built by Valiant Labs using high-quality technical datasets. Perfect for engineers, architects, and AI developers.*

Repository: localaiLicense: apache-2.0

clm-v0.1-8b-vllm-cpp
CLM is a bi-encoder decision model from Contrastive-LM. A frozen Qwen3-8B backbone encodes the state and each candidate answer separately, two MLP heads project them into a 512-dimensional space, and the answer distribution is a softmax over the cosine similarities at scale 100. It answers typed choice, noul and score questions and does not generate text. This entry installs a converted redistribution: the CLM-v0.1-8B heads as head.safetensors next to the unchanged Qwen3-8B backbone and tokenizer, with config.json naming the ClmModel architecture. The checkpoint only works with vllm.cpp (the vllm-cpp backend); transformers, vLLM and llama.cpp cannot load it. In LocalAI, serve it via POST /v1/systemone. Put the question in instructions: the state head reads the state followed by the instructions. The vllm.cpp project compared this checkpoint on CPU with the reference code over eight questions: 8 of 8 answers agree, largest probability difference 0.029 against the reference in bf16 (transformers stood in for the reference's GPU encoder). This entry was installed and served through LocalAI on CPU and gave the model card's example answer (person, 0.950). There is no accuracy benchmark and no GPU run. The entry sets a 4096-token context and a KV pool that holds four such sequences. BF16 backbone with F32 heads, about 16.5 GB, pinned to a revision. On CPU the model needs about 19 GB of free RAM (measured peak 17.9 GB resident).

Repository: localaiLicense: apache-2.0

qwen3-vl-4b-vllm-cpp
Qwen3-VL-4B-Instruct on vllm.cpp, in bf16: a small vision-language model that takes images alongside text. In the engine's correctness battery the image path matches vLLM token for token, and video input is a near tie. Roughly 9 GB of weights plus KV cache at the context configured here. It runs where the flagship NVFP4 checkpoints cannot, including plain CPU.

Repository: localaiLicense: apache-2.0