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AI 模型 85003

• Llama-3.1-Tulu-3-8B-RM↗

  allenai

• Olmo-3-1025-7B↗

  allenai

  We introduce Olmo 3, a new family of 7B and 32B models. This suite includes Base, Instruct, and Think variants. The Base models were trained using a staged training approach. Olmo is a series of **O**pen **l**anguage **mo**dels designed to enable the science of language models. These models are trained on the Dolma 3 dataset. We are releasing all code, checkpoints, and associated training details. The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it's recommended to pass the inputs directly to CUDA using:

• Qwen3.5_2B_Finetune_Uncensor_GGUF_quantized↗

  amkkk

• Parable-Granite-4.1-8B-Claude-Fable-5-GGUF↗

  AnkitAI

  > **~6 GB of RAM is all you need.** The Q4 build fits comfortably on an ordinary laptop or a mid-range GPU. > One command and you have a private, offline reasoning model on your machine: > > Parable-Granite-4.1-8B is an ibm-granite/granite-4.1-8b fine-tune trained on real multi-step agent sessions: planning, tool use, and ` ` reasoning captured from actual Claude Fable 5 and GPT-5.5 agent work, not synthetic Q&A. It is the largest release in the Parable series. **🔮 v2 is coming.** The 3B just got the v2 treatment (13× corpus, rebuilt recipe) — the same upgrade lands here next. Same links, in-place.

• Claude Opus 5.5 (Medium, Default Fallback)↗

  Anthropic

• T-lite-it-1.0-Q4_K_M-GGUF↗

  aovchinnikov

• Ovis-Image-7B↗

  ATH-MaaS

• z13HDU34ioNHH47CLDiucgfgsl1kgd4h47xb↗

  Aveniats

• Qwen3.8-27B-FP8-dynamic↗

  AzatAI

• Qwen3.6-35B-A3B-abliterated-v4↗

  Bahushruth

  Uncensored version of Qwen/Qwen3.6-35B-A3B with refusal behavior removed via **abliteration** (norm-preserving orthogonalization). > **GGUF quantizations:** Bahushruth/Qwen3.6-35B-A3B-abliterated-v4-GGUF > > **Blog post:** Abliteration: Uncensoring LLMs via Weight Surgery Abliteration identifies the "refusal direction" in the model's residual stream — the linear direction that activates when the model decides to refuse — and surgically removes it from all output projection weights using norm-preserving orthogonalization.