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AI 模型 2119

• gpt_bigcode-santacoder↗

  bigcode

  This is the same model as SantaCoder but it can be loaded with transformers >=4.28.1 to use the GPTBigCode architecture. We refer the reader to the SantaCoder model page for full documentation about this model There are two versions (branches) of the model: * `main`: Uses the `gpt_bigcode` model. Requires the bigcode fork of transformers. * `main_custom`: Packaged with its modeling code. Requires `transformers>=4.27`. Alternatively, it can run on older versions by setting the configuration parameter `activation_function = "gelu_pytorch_tanh"`. The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. `# the following function computes the sqrt`) or write a function signature and docstring and let the model complete the function body.

• Ingot-XVI-27B-Q2↗

  dendriteholdings

  A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it. It is Prism ML's Ternary Bonsai 2 27B, refined: the same ternary codes, with the rest of its weights re-tuned by the miner to bring it closer to Qwen3.8-27B. The weights are the miner's upload, byte for byte (`Ingot-XVI-27B-PQ2_0.gguf`); `Ingot-XVI-27B-PTQ1_0.gguf` repacks them losslessly at 1.75 bits per weight. Credit belongs to the miner, and to Prism ML for Bonsai 2.

• Qwen3.8-27B-swift15-uncensored-ajgazin-nvfp4full-dflash2-NInfer-v3↗

  kaushikvira

  An **all-NVFP4 abliterated** artifact of ukisai/Swift-1.5-Qwen3.8-27b for the NInfer engine (v3 container), with z-lab's DFlash2 speculative drafter and indexed proposal head embedded. Uncensored sibling of Qwen3.8-27B-swift15-nvfp4full-dflash2-NInfer-v3. All credit for the underlying model belongs to the original authors. This artifact is a format conversion + quantization + refusal-direction ablation — **no fine-tuning of our own**. 1. **Abliteration** — single-direction Arditi-style ablation: orcarouter's refusal direction r, recovered from their Qwen3.8-27B-Uncensored weights, projected out of 131 residual-writing tensors of Swift-1.5 (o_proj, GDN out_proj, mlp.down_proj, embed_tokens) in float32, exactly as ajgazin's release. 2. **All-NVFP4 quantization** with `llm-compressor` (512 Ultrachat calibration samples, seq 2048, sequential CPU pipeline): NVFP4 W4A4 group-16 on every text projection, W8G32 token embedding + output head, official q6/q8 vision allocation, MTP + DFlash2 BF16. 3. **Global-divisor normalization** across each fused packing group (worst re-encode rel err 79.25%), then v3 conversion with the fork converter (`--components text,vision,mtp,dflash2 --proposal`).

• DongfuJiang_-_prm_version2_subsample_hf-gguf↗

  RichardErkhov

• matchanything_eloftr↗

  zju-community

  The MatchAnything-ELOFTR model was proposed in **"MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training"** by Xingyi He, Hao Yu, Sida Peng, Dongli Tan, Zehong Shen, Hujun Bao, and Xiaowei Zhou from Zhejiang University and Shandong University. This model is a version of **ELOFTR** enhanced by the MatchAnything pre-training framework. This framework enables the model to achieve universal cross-modality image matching capabilities, overcoming the significant challenge of matching images with drastic appearance changes due to different imaging principles (e.g., thermal vs. visible, CT vs. MRI). This is achieved by pre-training on a massive, diverse dataset synthesized with cross-modal stimulus signals, teaching the model to recognize fundamental, appearance-insensitive structures. "Image matching, which aims to identify corresponding pixel locations between images, is crucial in a wide range of scientific disciplines, aiding in image registration, fusion, and analysis. In recent years, deep learning-based image matching algorithms have dramatically outperformed humans in rapidly and accurately finding large amounts of correspondences. However, when dealing with images captured under different imaging modalities that result in significant appearance changes, the performance of these algorithms often deteriorates due to the scarcity of annotated cross-modal training data. This limitation hinders applications in various fields that rely on multiple image modalities to obtain complementary information. To address this challenge, we propose a large-scale pre-training framework that utilizes synthetic cross-modal training signals, incorporating diverse data from various sources, to train models to recognize and match fundamental structures across images. This capability is transferable to real-world, unseen cross-modality image matching tasks. Our key finding is that the matching model trained with our framework achieves remarkable generalizability across more than eight unseen cross-modality registration tasks using the same network weight, substantially outperforming existing methods, whether designed for generalization or tailored for spe

• Qwen3.8-27B-DFlash2-NVFP4-RTNcal↗

  maurienne-ai

  Calibrated **NVFP4 (W4A4)** quantization of the DFlash 2 block-diffusion draft model for **Qwen3.8-27B**, packaged in the NVIDIA ModelOpt layout that SGLang loads natively. Built for a single **RTX 5090 (32 GB)** serving Qwen3.8-27B in NVFP4 with SGLang: the draft shrinks from **3.53 GB (BF16) to 1.37 GB** of VRAM, which turns into **+44 % KV-cache context (90K → 130K tokens)** at the same decode speed and the same acceptance rate as the BF16 draft. Output quality is unchanged by construction: the target model verifies every drafted token. \* Target `gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090`, FP8 KV cache, `--mem-fraction-static 0.90`, HiCache on. Benchmark: 5 mixed prompts (code, math, French prose, JSON, essay) × 600 generated tokens, 3 runs each, temperature 0.7. Run-to-run noise on the acceptance length is about ±0.2, so BF16 and this checkpoint are at parity.

• Signal-3.8-27B-GGUF↗

  agentionai

  > [!IMPORTANT] > **Recommended settings** > Temperature 0.6, top-p 0.95, top-k 20, min-p 0.05, **presence_penalty 1.0**, KV cache q8_0 or f16. > Keep min-p and presence_penalty on: without them, long generations can run into repetition on some > draws (a 4-bit value cache makes that deterministic). If you enable the MTP draft head on recent > mainline llama.cpp, also pass **`--no-spec-draft-backend-sampling`** — the default backend draft > sampling does not apply your sampler to accepted draft tokens, which can let repetition run away > regardless of the settings above. > [!WARNING] > The tiers in this repository were rebuilt on 2026-09-13. The first release's tiers could, rarely, > repeat an answer line when sampling without the draft head; the rebuilt tiers showed no loop or cap > in 55 traces under the same conditions. If you downloaded before that date, re-download. AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-27B designed for lower generation latency and better token efficiency. On our held-out general-prompt evaluation, Signal produces **25% fewer answer tokens** and uses **41% fewer thinking tokens**, while matching or improving the measured answer quality of the base model.

• Qwen3.8-27B-mmproj-Q5_K-MIX-GGUF↗

  HermiHg

  A **mixed-precision 4–6-bit quantization** of the multimodal projector (`mmproj` — the vision encoder and projector tensors) for `Qwen/Qwen3.8-27B`, built to be close to **a third of the size** of the upstream BF16 checkpoint (331 MiB vs 888 MiB) while staying within ~0.5% of its accuracy on an 11-benchmark vision suite. `Q5_K-MIX` is a mixed-precision quant that keeps the attention and FFN projections at 5-bit, holds the large FFN down-projection at 6-bit, and compresses the small vision-to-LM projectors hardest. The measured gap to the reference (net +18 failing documents, -0.0035 mean) is smaller than the run-to-run noise band the test protocol shows on reference-quality models, so we read it as within the measurement noise of the evaluation rather than a real degradation. Sub-4-bit variants of the same recipe were evaluated and showed regressions beyond that band, so the 5/6-bit mix is the smallest configuration that stayed within it on every benchmark. Both projectors served with `llama-server` against the `Qwen3.8-27B` target on a 24 GB NVIDIA GPU, evaluated with `lmms-eval` at temperature 0 (deterministic): 5,251 paired documents across 11 vision benchmarks (TextVQA, OCRBench-v2, GQA, ScienceQA, MMStar, ChartQA, and five BLINK perception categories), identical rows for both checkpoints.

• Unsloth-Ornith-1.5-35B-A3B↗

  peculiar-ragdoll

  An imatrix-calibrated, **Unsloth-Dynamic-style** GGUF ladder for **Ornith-1.5-35B-A3B** — the model's own weights and chat template, re-quantized with a per-tensor precision policy and a fresh importance matrix. **The MTP (`nextn`) block is removed from every tier** — Ornith-1.5 ships it untrained, so it could only add size. Why. > ### Not affiliated with Unsloth > This is an independent, best-effort community reproduction of Unsloth's > Dynamic GGUF method — **not** an > official Unsloth release, and not endorsed by or connected to Unsloth in any way. Unsloth had not > published their own Ornith-1.5 quants at the time of writing and a lot of people wanted them, so > we published these while we wait. If and when Unsloth ship an official build, use theirs. "Unsloth" > is used here only to name the *method* these quants reproduce. 1. **Unsloth-Dynamic per-tensor policy.** Instead of one bit-width for the whole model, each tensor gets a type chosen by sensitivity — more bits where they matter (embeddings, attention, early/late layers), fewer where they don't. We transfer the published per-tensor recipe from Unsloth's `Qwen3.6-35B-A3B-GGUF` (same `qwen35moe` architecture) tier-for-tier. 2. **A fresh importance matrix.** Computed on Ornith's own `BF16` over an interleaved multilingual + code calibration corpus, so the quantizer knows which weights to protect for *this* model rather than inheriting someone else's statistics.

• industry-classification↗

  sampathkethineedi