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View details ↗DISCOVER & COMPARE
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Qwen3.6-27B-MTP-GGUF
froggeric基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗**1. Architecture-aware uncensoring.** Qwen 3.6 uses a hybrid attention design — linear (DeltaNet-style) *and* traditional softmax blocks, mixed 3:1. Most abliteration tools treat them the same. llmfan46 applied **separate parameters** for each attention type using the Heretic tool, yielding one of the lowest KL divergences (0.0015) of any uncensored Qwen variant — 88% fewer refusals with negligible capability loss. **2. A fixed chat template.** The official Qwen 3.6 template is broken on every C++ runtime (LM Studio, llama.cpp, MLX). Tool calls crash, the `developer` role throws errors, and empty thinking blocks waste your context window. This model ships with a rewritten template that fixes all five issues and adds a thinking toggle (` ` / ` `) you can drop into any message. **3. Vision, fixed and working.** The source model had 333 vision tower keys with incorrect prefixes, breaking image inputs. Those were corrected before conversion, so text, image, and video inputs all work out of the box.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗WestLake-10.7B-v2-GGUF
froggeric基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗50M-pro
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View details ↗ZeroS-Pico-v1.0
fromziro基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ZeroS-Qana-5M
fromziro基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ZeroS-v0.1-150M
fromziro基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LocalAI
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View details ↗jebadiah-27b
frontier-infra基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗jebadiah-27b-GGUF
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View details ↗jebadiah-27b-v2-1-GGUF
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View details ↗jebadiah-4b-v2
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View details ↗jebadiah-4b-v2-GGUF
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View details ↗jebadiah-9b-v2
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View details ↗jebadiah-9b-v2-1-GGUF
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View details ↗jebadiah-9b-v2-GGUF
frontier-infra> **Just want to run it?** Download jebadiah-9b-v2-1-GGUF and open it in llama.cpp, Ollama or LM Studio. > > **Which one should I use?** Start with the 9B. It's smaller and faster, and it's what most people run. Pick the 27B if you have the memory and want the higher score (Decision Index Full 55.94 against the 9B's 44.91, board data of 2026-10-15). On Apple silicon, use the MLX builds of the same models. You can try the 4B in the browser first: jebadiah-playground. > > This repo: the 9B v2 GGUF quants (Q4_K_M, Q5_K_M, Q8_0), for llama.cpp, Ollama and LM Studio. **Jeb is a decision model, not a chat model.** Use `jeb serve` or the decide scripts (see Run Jeb locally on jebadiah.ai). In an LM Studio or Ollama chat window it will look wrong. Trying it in a chat window? Use the optional chat profiles: https://github.com/getainode/jebadiah/blob/main/docs/chat-window.md
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View details ↗Lumma-fev-0.1b
FrontiersMindLumma-Fev-0.1B is a **decision model**. It reads one document (the *state*) and a set of typed questions, and returns a probability distribution for each question in a single forward pass. It never generates text, so there is nothing to parse and nothing to hallucinate. 🔥 **See our smallest Lumma-fev 150 million model turn decisions into actions** - playing **Super Mario autonomously and in real time** on a **MacBook Air M2**. ⚡ ⚡ Despite its compact size, Lumma-fev can **observe the game state, reason about the situation, and choose the next action** — all running **locally on consumer hardware**. 💻
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View details ↗Lumma-fev-0.6b
FrontiersMindLumma-Fev-0.6B is a **decision model**. It reads one document (the *state*) and a set of typed questions, and returns a probability distribution for each question in a single forward pass. It never generates text, so there is nothing to parse and nothing to hallucinate. `state` can be text, a JSON object or an array. Pin a version with `revision=" "`, and move the model to a GPU with `model.to("cuda")` (bf16 is the stored precision). `lumma-fev-serve` exposes the TypeSafe `POST /v1/systemone` contract, so existing TypeSafe clients work by changing their base URL.
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View details ↗Lumma-fev-27B-GGUF
FrontiersMind基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Lumma-fev-4b
FrontiersMindLumma-Fev-4B is a decision model developed by FrontiersMind. It reads one document (the state) and a set of typed questions, and returns a probability distribution for each question in a single forward pass. It never generates text, so there is nothing to parse and nothing to hallucinate. `state` can be text, a JSON object or an array. Pin a version with `revision=" "`, and move the model to a GPU with `model.to("cuda")` (bf16 is the stored precision). `lumma-fev-serve` exposes the TypeSafe `POST /v1/systemone` contract, so existing TypeSafe clients work by changing their base URL.
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View details ↗Frontis-MA1-30B-SFT
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