detector-scope
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roberta-large-finetuned-race
LIAMF-USP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 ↗ai-image-detector-model
LiamReardon01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 ↗timee
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View details ↗Llama-3.2-Taiwan-3B-Instruct-GGUF
lianghsun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 ↗win98-gemma-270m-onnx
lianghsun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 ↗Qwen3-8B-CC-SFT-v1
liangzhidanta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 ↗Qwen3-8B-CC-SFT-v2
liangzhidanta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 ↗Qwen3-8B-CC-SFT-v2-2e
liangzhidanta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 ↗Wan2.2-TI2V-5B-Turbo-GGUF
liaqateagle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 ↗LTX-2.3-GGUF
Libai111111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 ↗DeepSeek-V4.1-Flash-1017-EXL3-2.9bpw
LibertAIDAI**TL;DR** — EXL3 quantization of deepseek-ai/DeepSeek-V4.1-Flash-1017 at **2.90 bpw** (196.3 GiB). On held-out text its perplexity is **+1.8% (code) / +0.4% (prose)** above the unquantized model. Tensor names, dtypes and per-tensor bitrates match Mia-AiLab/DeepSeek-V4.1-Flash-EXL3-2.9bpw tensor for tensor, so the MiaAI-Lab/DeepSeek-v4.1-Flash-EXL3-2x-DGX-Sparks serving kit's bundled K map describes it unchanged. The two Engram tables are **not** in this repo: take them from the 1017 base repo, because they changed in this release. > **Chat template:** this repo ships `chat_template.jinja`, a Jinja port of DeepSeek's 1017 `encoding/encoding.py` (from jayleaton's MIT template via a community PR by dpolist, amended for the 1017 encoder). It renders byte-identically to the reference on our 8,455-case test corpus. Runtimes that require a template file at startup need it (verified to render identically under Python Jinja and TensorFold's Zig engine); vLLM-based kits use their built-in DeepSeek encoder. > **Agentic use / reasoning effort:** with tools in the conversation, each turn's reasoning stays in the prompt and is re-read by every later turn, so reasoning cost compounds. On our benchmark of the original 1017 weights (3,738 jobs: agentic coding, tau-bench, LiveCodeBench, AIME, chat), `reasoning_effort` **high** (75, the default) matched **max** (100) on quality at 0.55–0.8× the cost, and **low** (50) matched too at 0.4–0.6× (results in discussion #4). Avoid max for agent loops unless you need the headroom.
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View details ↗DeepSeek-V4.1-Flash-1017-EXL3-3.5bpw
LibertAIDAI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 ↗DeepSeek-V4.1-Flash-1017-NVFP4
LibertAIDAI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 ↗DeepSeek-V4.1-Flash-NVFP4
LibertAIDAI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 ↗Gemma-4-12B-IT-NVFP4
LibertAIDAI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 ↗Gemma-4-12B-IT-NVFP4-GGUF
LibertAIDAI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 ↗Gemma-4-31B-IT-NVFP4-GGUF
LibertAIDAI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 ↗GLM-5.3-Flash-NVFP4
LibertAIDAI*Weight-only NVFP4 quantization of Z.ai's GLM-5.3-Flash — the first GLM to combine **sparse and linear attention**.* A **weight-only NVFP4 (NVFP4-A16)** checkpoint. The **routed-expert FFN** tensors — 97% of the model's parameters — are quantized to **NVFP4** (E2M1, with FP8-E4M3 per-16-block scales and an FP32 per-tensor global scale). Everything outlier-sensitive stays in **BF16**: > both attention flavours (all 34 KDA linear-attention layers *and* the 11 DeepSeek-sparse layers, including the sparse indexer) · the **entire vision tower** · shared experts · MoE routers · dense-MLP layers · the MTP head · the Manifold-Constrained Hyper-Connection (mHC) tensors · token embeddings · `lm_head` · all norms
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View details ↗Nex-N2-mini-GGUF
LibertAIDAI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 ↗Qwen3.6-35B-A3B-NVFP4-GGUF
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View details ↗Qwen3.6-35B-A3B-NVFP4-MTP-GGUF
LibertAIDAI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 ↗Qwen3.8-27B-NVFP4-GGUF
LibertAIDAINVFP4 GGUF quantizations of Qwen/Qwen3.8-27B for llama.cpp, built for native NVFP4 tensor-core math on Blackwell GPUs. The NVFP4 weights come from our own ModelOpt post-training quantization of the BF16 release, using NVIDIA's dense recipe: NVFP4 at group size 16 on the language-model MLP linears only, calibrated on 512 samples of cnn_dailymail at sequence length 512. Attention (both the full-attention and Gated-DeltaNet linear-attention layers), the vision tower, the MTP block, embeddings and `lm_head` are left in BF16. That is 192 quantized linears, 64 layers by gate, up and down. An MTP variant with the speculative draft head is published separately at Qwen3.8-27B-NVFP4-MTP-GGUF.
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View details ↗Qwen3.8-27B-NVFP4-MTP-GGUF
LibertAIDAI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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