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AI モデル 2119
- Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4↗
AEON-7
> [!IMPORTANT] > **Superseded by Qwen3.8 — use this instead:** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-NVFP4-MIXED` > > **Operator recipes (Spark Dynamic DFlash lattice, TP=2, RTX):** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED` > > This Qwen3.6-27B card stays online for history and existing downloads. For new work, move to the Qwen3.8 Ultimate Uncensored NVFP4-MIXED release. That line is far superior in capability and reaches substantially more of what the architecture can do, with a more refined mixed quantization methodology (NVFP4 + FP8 lattice) than these older Qwen3.6 exports. > > Serve with the unified container: `ghcr.io/aeon-7/aeon-vllm-ultimate:latest` (Spark / GB10) or `ghcr.io/aeon-7/aeon-vllm-ultimate-rtx:latest` (RTX discrete). NVFP4 hardware-quantized release of `AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored`. Same model, same 0/50 refusal rate, same preserved capabilities — now compressed from **51 GB BF16** to **26 GB NVFP4** for native FP4 hardware throughput on DGX Spark / GB10 / Blackwell sm_121a. This release is **multimodal-preserved** (vision tower stays BF16 — text + image inference fully functional) and **hybrid-attention-preserved** (the 48 linear-attention / GatedDeltaNet layers stay BF16; FP4 only applies to the 16 full-attention layers' output projections + all MLPs).
- distilroberta-base↗
distilbert
1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. How To Get Started With the Model This model is a distilled version of the RoBERTa-base model. It follows the same training procedure as DistilBERT. The code for the distillation process can be found here. This model is case-sensitive: it makes a difference between english and English. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base.
- Cydonia-24B-v4.3-heretic-nvfp4↗
Firworks
**Format:** NVFP4 — weights & activations quantized to FP4 with dual scaling. **Base model:** `coder3101/Cydonia-24B-v4.3-heretic` **How it was made:** One-shot calibration with LLM Compressor (NVFP4 recipe), long-seq calibration (256 samples of 4096 length) with xensive/roleplaydataset100k. If there are other models you're interested in seeing quantized to NVFP4 for use on the DGX Spark, or other modern Blackwell (or newer) cards let me know. I'm trying to make more NVFP4 models available to allow more people to try them out.
- Qwen3.8-27B-DFlash2-ternary-bonsai2↗
naklitechie
A DFlash 2 draft model for prism-ml/Ternary-Bonsai-2-27B (PrismML's 2-bit, Hadamard-rotated Qwen3.8-27B). It is z-lab/Qwen3.8-27B-DFlash2 fine-tuned on the ternary model's own hidden states and its own greedy generations, so the drafter predicts what the 2-bit target will actually say rather than what the bf16 base would have said. Same architecture and config as the z-lab checkpoint (5 Qwen3-style layers, hidden 5120, 32/8 heads, sliding window 2048, `block_size 8`, taps at target layers 5/19/33/47/61, rank-256 candidate selector, top-k 16, `mask_token_id 248070`); shares the target's embeddings and `lm_head`, so there are no `lm_head`/embedding tensors here. 81 bf16 tensors, 3.85 GB. `config.json` is the z-lab file, unchanged. Runtime: NakliTechie/dflash-mlx-bonsai2 (Apple Silicon, MLX). Status: experimental; measured on one M4 Pro.
- MiniCPM5-1B-SFT↗
openbmb
MiniCPM Tech Report | MiniCPM Wiki(Chinese) | GitHub Repo | UltraData | MiniCPM Desk Pet | Online Demo We are releasing **MiniCPM5-1B**, the first model in the **MiniCPM5** series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA. 🏆 **1B-class open-source SOTA**: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.
- sd-turbo↗
Stability AI
SD-Turbo is a distilled version of Stable Diffusion 2.1, trained for real-time synthesis. SD-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high image quality. This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. - **Developed by:** Stability AI - **Funded by:** Stability AI - **Model type:** Generative text-to-image model - **Finetuned from model:** Stable Diffusion 2.1 For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference).
- Yi-1.5-9B-32K↗
01-ai
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T
- flan-t5-xxl↗
Google
0. TL;DR 1. Model Details 2. Usage 3. Uses 4. Bias, Risks, and Limitations 5. Training Details 6. Evaluation 7. Environmental Impact 8. Citation If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : > Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the T5 model card.
- Tess-2.0-Llama-3-8B↗
migtissera
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-8B was trained on the meta-llama/Meta-Llama-3-8B base. Tess-2.0-Llama-3 was trained on the (still curating) Tess-2.0 dataset. Tess-2.0 dataset contains ~100K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions. The model was only fine-tuned for 1-epoch with a low learning rate to try and preserve its entropy as much as possible.
- NerdySamantha-i1-GGUF↗
mradermacher