This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.1117 - Matthews Correlation: 0.5324 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5
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View details ↗This is a sentence-transformers model finetuned with wandb-ai from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. For this case, it uses for column standardization. - **Model Type:** Sentence Transformer - **Base model:** sentence-transformers/all-MiniLM-L6-v2 - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face
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View details ↗**The open PR is merged on 2022/9/14.** You can use this model with v4.23 and higher versions of transformers as follows, This repository provides a 2.7B-parameter Japanese GPT-NeoX-based model. The model was trained by ABEJA, Inc The model uses a special sub-word tokenizer. Please refer the original repository or GPT-NeoX-Japanese in detail.
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View details ↗GGUF quantizations of apodex/Apodex-1.1-mini, a 35.95B-parameter Qwen3.5 MoE model for research, data, files, code, and tool-driven work. All model files retain the checkpoint's native MTP/NextN head. For image input, use the included F16 vision projector. *Results reported by Apodex AI from evaluations of the original Apodex-1.1 model using FrontierAgent, its agent harness and evaluation suite.*
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View details ↗GGUF quantizations of Cloudflare/clef, a 27B multimodal decision model post-trained from Qwen3.8-27B. For image or video input, use the included multimodal projector (`clef-mmproj-BF16.gguf`). Tested with `torch` 2.11 and `transformers` 5.10.2 on a single H200. Image and video inputs also need `pillow`. `systemone` takes a Jev/SystemOne `POST /v1/systemone` request body and returns the same response body: `model`, `answers` keyed by question ID, and `usage`. A `choice` answer has `choice`, `confidence`, and `probabilities`; a `score` answer has the expected `score`, `confidence`, `legend`, and `probabilities`; a `noul` answer has the probability of true. `instructions` is optional, and `images` and `videos` may be added to the request.
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View details ↗Mixed-precision **GSQ-RCO** GGUF quantizations of Nanbeige/Nanbeige4.3-33B, optimized for efficient, high-fidelity local inference in `llama.cpp`. **64k Long-Context (Low-VRAM)** Using 4-bit KV cache (`-ctk q4_0 -ctv q4_0`) allows 65k context to run within **< 12 GB total VRAM** on consumer 12 GB GPUs (e.g. RTX 3060 12GB, RTX 4070):
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View details ↗Mixed-precision **GSQ-RCO** GGUF quantizations of nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16, optimized for efficient, high-fidelity local inference in `llama.cpp`. The model supports a context length of up to 65,536 tokens (64K). Standard quantization severely degrades hybrid sliding-attention models with shared Key-Value caches, multimodal projections, and speculative draft targets. **GSQ-RCO** solves this through two targeted techniques: - **RCO:** Preserves all 54 shared `k_proj`, `v_proj`, and `norm` tensors across layers 24–41 in high precision (`Q5_K`/`Q6_K`), maintaining structural attention geometry, visual projection alignment, and DFlash draft compatibility. - **GSQ:** Optimizes per-layer precision guided by Hessian sensitivity calibration.
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View details ↗Mixed-precision **GSQ-RCO** GGUF quantizations of TokenRhythm/NeoHorse-1-9B, optimized for efficient, high-fidelity local inference in `llama.cpp`. The model supports a context length of up to 262,144 tokens (262K). Standard quantization severely degrades hybrid SSM-Transformer models (e.g. stock low-bit quantizations cause recurrent state drift and degradation in reasoning coherence). **GSQ-RCO** solves this through two targeted techniques: - **RCO:** Keeps all 48 critical SSM parameters in unquantized **BF16**. - **GSQ:** Optimizes per-layer precision guided by Hessian sensitivity calibration.
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