Open-Weight Models Under Apache License 2.0

Model · Text generation

Decision-1-NF4

Worthify AI

A pre-quantized NF4 checkpoint in the Worthify Decision-1 family. Download 7.64 GB of weight shards and load the saved NF4 tensors directly. In three short usage checks on an RTX 3090, peak PyTorch-allocated inference memory was 7.26 GiB. We converted Decision-1 at cab55818cfb6f4163f2b2597bed31cceb6ba2030 with bitsandbytes NF4 and double quantization. This release adds no training. Decision-1 is a single-seed research preview derived from Gemma 4 12B; its original model card describes the training and BF16 evaluation. Give the model a text state, a question, and 2–16 described options. Read the next-token scores for the supplied option letters to select an option ID. Those scores are…

Model · Text classification

decision-4b-gguf

Eval Engine

GGUF builds of Decision-4B, an open-weight Jev-like decision model from Eval Engine, the AI arm of Chromia. Give it a state, a question, and a list of options. It answers with one letter. Runs in llama.cpp, Ollama, and on your phone. The Q4KM file is the one that powers Decision-4B in the Unbound app. All three are the LoRA merged into Qwen3.5-4B. The BF16 adapter scores 87.3% on the same panel. Use Q4KM for phones and laptops, Q80 or F16 when you have the memory. Full table and details on the adapter card. The reply is a single letter. toplogprobs gives the score for each option letter; softmax over the listed letters gives a probability per option. Input is a JSON object with state…

Model · Image and text to text

DeepSeek-V4.1-Flash-TensorFold-TP2-2xGB10

Bertholomus AI

BertholomusAI recipe: DeepSeek-V4.1-Flash served tensor-parallel over two NVIDIA DGX Spark / GB10 nodes by a deepseekv41 model family we wrote for TensorFold (by ashhart). Clean-room: the model math is re-implemented from DeepSeek's MIT inference code and tech report; no code from other DeepSeek-V4.1 recipes or kits was read or copied (ATTRIBUTION.md). Decode and prefill are what we worked on this time. The same release suite on the served lane for both builds (v0.4 = engine bd0024d at --context 262144, v0.5 = 808eb4a at --context 1048576), same client (tools/dsv41/kitbench.py), same prompts, greedy, DSpark drafting. Median of 3 unless noted. four long streams together give the same replies…

Model

deit-classification

Fiona Reyes

A research-oriented DeiT prototype targeting Classification. The included large setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a linear warmup schedule. These are starting values in the script, not evidence of a completed run. For…

Model · Text generation

DeltaRoutingAI

HorneSci

HorneSci's open-weight continued-pretraining release for Qwen3.8-27B. DeltaRoutingAI trains language adapters on Python technical documentation and delivers a 4.61% reduction in held-out text NLL in its paired evaluation. The downloadable checkpoint is a PEFT LoRA adapter in safetensors format (233.6 MB). Load it with Qwen/Qwen3.8-27B, pinned to revision 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0. Qwen authors the base model; HorneSci authors the continued-pretraining recipe and adapter weights. The adapter weights are released under Apache 2.0 with upstream attribution and data notices included. Use a CUDA environment with the versions in requirements-inference.txt. The evaluated environment…

Model · Robotics

demodoctor-act-clean-s2

Akteruzzaman Raihan Sikder

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Model · Depth estimation

Depth-Anything-3-Small-LiteRT

LiteRT Community (FKA TFLite)

On-device LiteRT / TFLite conversion of Depth Anything 3 — Small (ByteDance-Seed, Apache-2.0) for monocular depth, running fully on the mobile GPU via the LiteRT CompiledModel API (ML Drift delegate). No CPU fallback ops — the whole graph is GPU-compatible. DA3 processes images at their native aspect ratio (upperboundresize, longer side → 896, multiple of 14). Forcing a square 896×896 and letterbox-padding drops the match to corr 0.977 (the black padding leaks into the content through global attention). Converting at the native rectangle restores corr 0.9994 and is also faster (fewer tokens). This checkpoint is built for portrait ~9:16. For another aspect, re-convert at that shape (or your…

Dev is an open-source 399M parameter bidirectional decision model built on ModernBERT-large. It is purpose-built for unstructured-to-structured classification—including ticket routing, yes/no verification, and rating scales—executing in a single forward pass (~28ms on MPS) without token generation. Following Jev (TypeSafe) and Kev-0.5B (Jared Palmer), Dev tests a fundamental architectural question: What if decision models shouldn't be causal decoders at all, but native bidirectional cross-encoders? Evaluated against Jared Palmer's kev-0.5b (built on a frozen Qwen-2.5-0.5B causal backbone + 9.3M LoRA pointer head): Inference Latency: Dev-0.4B executes in 27.6ms on Apple Silicon MPS (M1 Max)…

Model · Text generation

DietRecommendation-Qwen2.5-0.5B

Yubraj Sigdel

A LoRA adapter for Qwen/Qwen2.5-0.5B-Instruct that turns a short user profile (age, gender, height, weight, activity level, dietary preference, daily calorie target) into a one-day meal plan with breakfast, lunch, snack and dinner. It is a small, fast, single-purpose model: about 2.2M trainable parameters on top of a 0.5B base, usable on CPU. The adapter was trained on one fixed prompt layout. Use the same system prompt and the same field names and order, otherwise quality drops. Two details matter: - Pass the system prompt explicitly. Without it, the chat template inserts Qwen's default system prompt, which the adapter never saw. - The first field is spelled Ages: in the training data, so…

Model · Robotics

diffusion_blocos_pote

Victor Ivar van Halst

Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. This policy has been trained and pushed to the Hub using LeRobot. See the full LeRobot documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI cheat-sheet — quick reference for the lerobot- commands. The short…

Model

dino-classification6

Pedro

A research-oriented Dino prototype targeting Classification. The included xlarge setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a cosine schedule. These are starting values in the script, not evidence of a completed run. For a…

Model

dino-demo

Krish

A research-oriented Dino prototype targeting Retrieval. The included nano setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a onecycle schedule. These are starting values in the script, not evidence of a completed run. For a…

Model

dino-generation

S. Young

Working implementation of Dino for Generation using a small configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses rmsprop with a exponential schedule. These are starting values in the script, not evidence of a…

Model

dino-retrieval69

Christopher Garcia

Working implementation of Dino for Retrieval using a base configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a constant warmup schedule. These are starting values in the script, not evidence of a…

DINO helps researchers find visually similar images without needing descriptions or predefined categories. It can be useful when exploring collections of historical photographs, artworks, or other digitized images. DINO can be used to find visually related images, compare images, and explore patterns within large collections. Unlike CLIP, it does not support searching images using text descriptions. Visual similarity does not necessarily mean historical or cultural similarity. Results should be checked against the research context, especially when working with specialist collections. The EIDORA package resizes images to 224 × 224 pixels using bilinear interpolation. This may differ from the…

Model · Text generation

Diplodocus-preview-GGUF

Osaurus-AI

Diplodocus preview 4, the Osaurus assistant for finance, documents, spreadsheets and tool-assisted work, built on IFM/K2-Horizon-7B and trained on Osaurus' own workspace tools (workbooks and documents edited in place, the built-in calculate tool, reasoning on and off). This is the GGUF build for llama.cpp: one calibrated Q6K file, 7.76 GB. Using Osaurus on a Mac? Use the MLX bundle OsaurusAI/Diplodocus-preview-JANG6M instead. Same weights, same template, same reasoning toggle. Requires llama.cpp with K2 Horizon support (ggml-org/llama.cpp#29535, merged 2026-10-06). Older builds, and apps that bundle an older llama.cpp, will not load the k2-horizon architecture. Trained rounds R8–R11 on top…

Model

Discrepancy_Calculus

Convergent Intelligence

A Referential Introduction to the Measure-Theoretic Framework for Singular Analysis and Structure-Aware Machine Learning Roy C March 2026 We present the core definitions, axioms, and principal theorems of Discrepancy Calculus (DISC) — a measure-theoretic framework that treats singularities as primary mathematical structure rather than pathology. The central object is the discrepancy operator, which quantifies the mismatch between integration and differentiation on metric-measure spaces; classical calculus is recovered as a degenerate smooth limit. We state the eight axioms of DISC, prove the Mesh Fundamental Identity (the DISC replacement for the Fundamental Theorem of Calculus), introduce…

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 64 - evalbatchsize: 64 - lrschedulertype: linear - numepochs: 2 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2

Model · Text classification

distilbert-food-not-food

Saber Bounehas

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - Transformers 5.18.0 - Pytorch 2.11.0+cpu - Datasets 5.0.1 - Tokenizers 0.23.2

LoRA adapters from a research sprint asking whether safety transfers subliminally through distillation. Report and code: https://github.com/Realmbird/distilling-safety (see reports/REPORT.md). Research artifacts, not for deployment. Several students are measurably less safe than the base model (that is the main finding); none of these adapters should be used as a safety intervention. All adapters sit on Qwen/Qwen2.5-7B-Instruct ("M0"). Teacher recipe: LoRA r=64, alpha=64, all attention and MLP projections; lr 1e-4, cosine schedule, 1 epoch, batch 16, max length 1024; completion-only loss. Data: LLM-LAT/harmful-dataset (2.5k-prompt training split) and LLM-LAT/benign-dataset (2.5k rows)…

This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3.0 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2

Model

dl-classification

Christian Krause

A small Mobilevit implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The nano variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a linear warmup schedule. These are starting values in the script…

Model · Image and text to text

DN-MOPD-Qwen3.5-2B-160updates

XinLi

A Qwen3.5-2B student trained with DN-MOPD (Domain-Normalized Multi-Teacher On-Policy Distillation) continued to 160 updates (paper Table 5). Three same-size RL experts (math, code, instruction following) teach one student on its own responses; each prompt is scored by the expert of its domain, and DN-MOPD rescales each domain's token-level feedback by its measured spread, wd = clip(σall / σd, 0.25, 4), so that no domain dominates the shared update. Paper: Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation (arXiv:2609.35347, project page) · Code: github.com/LiXin97/DN-MOPD The full recipe, with the launch scripts for every row of the paper's tables, is in…

Model · Image and text to text

DN-MOPD-Qwen3.5-2B-baseline-label-160updates

XinLi

The Label baseline of the DN-MOPD paper at Qwen3.5-2B continued to 160 updates (paper Table 5): multi-teacher on-policy distillation with label routing (each prompt is scored by the expert of its domain, every domain multiplier is 1). Released for comparison with DN-MOPD-Qwen3.5-2B; it is not the proposed method. Paper: Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation (arXiv:2609.35347, project page) · Code: github.com/LiXin97/DN-MOPD The full recipe, with the launch scripts for every row of the paper's tables, is in recipes/qwen3.5/ and docs/recipe.md. This model was trained and evaluated with the non-thinking chat format. Pass enablethinking=False to the…

Model · Image and text to text

DN-MOPD-Qwen3.5-4B-160updates

XinLi

A Qwen3.5-4B student trained with DN-MOPD (Domain-Normalized Multi-Teacher On-Policy Distillation) continued to 160 updates (paper Table 5). Three same-size RL experts (math, code, instruction following) teach one student on its own responses; each prompt is scored by the expert of its domain, and DN-MOPD rescales each domain's token-level feedback by its measured spread, wd = clip(σall / σd, 0.25, 4), so that no domain dominates the shared update. Paper: Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation (arXiv:2609.35347, project page) · Code: github.com/LiXin97/DN-MOPD The full recipe, with the launch scripts for every row of the paper's tables, is in…

Model · Image and text to text

DN-MOPD-Qwen3.5-4B-baseline-label-160updates

XinLi

The Label baseline of the DN-MOPD paper at Qwen3.5-4B continued to 160 updates (paper Table 5): multi-teacher on-policy distillation with label routing (each prompt is scored by the expert of its domain, every domain multiplier is 1). Released for comparison with DN-MOPD-Qwen3.5-4B; it is not the proposed method. Paper: Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation (arXiv:2609.35347, project page) · Code: github.com/LiXin97/DN-MOPD The full recipe, with the launch scripts for every row of the paper's tables, is in recipes/qwen3.5/ and docs/recipe.md. This model was trained and evaluated with the non-thinking chat format. Pass enablethinking=False to the…

Model · Image and text to text

DN-MOPD-Qwen3.5-9B-baseline-label-160updates

XinLi

The Label baseline of the DN-MOPD paper at Qwen3.5-9B continued to 160 updates (paper Table 5): multi-teacher on-policy distillation with label routing (each prompt is scored by the expert of its domain, every domain multiplier is 1). Released for comparison with DN-MOPD-Qwen3.5-9B; it is not the proposed method. Paper: Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation (arXiv:2609.35347, project page) · Code: github.com/LiXin97/DN-MOPD The full recipe, with the launch scripts for every row of the paper's tables, is in recipes/qwen3.5/ and docs/recipe.md. This model was trained and evaluated with the non-thinking chat format. Pass enablethinking=False to the…

Model · Image classification

document-moire-detector-ONNX

ONNX Community

This is an ONNX version of Jwalit/document-moire-detector. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for image-classification: https://huggingface.co/docs/transformers.js/api/pipelines#modulepipelines.ImageClassificationPipeline A fine-tuned DeiT-small Vision Transformer for detecting moiré patterns in document images. (common from screen photography, scanning, or screen captures). - clean (0): No moiré patterns - moire (1): Moiré patterns detected 1. Resize aliasing — downscale+upscale with NEAREST interpolation + pattern overlay 2. Pattern overlay — sinusoidal interference with per-channel color variation 3. Multi-frequency…

Model · Image segmentation

docverify

Ghost Sas

A forensic segmentation network that finds where a payslip was edited — copy-move, splice from another document, or a re-typed amount — and the small models and thresholds that turn its pixel map into a verdict an analyst can act on. Part of docverify, a complete system with arithmetic checks, PDF forensics and a local LLM note: TamperNet (~33M parameters): a pretrained ConvNeXt encoder on RGB, plus two forensic inputs — fixed SRM noise residual filters (edits break the sensor/print noise) and a JPEG/DCT branch (a pasted region carries its own 8×8 compression grid) — fused into a U-Net-style decoder that outputs one logit per pixel. The page score is the mean of the top-200 pixel logits.…

Model · Image to video

DreamX-Creator-gguf

EvoAwaken Workshop

Community GGUF conversion of GD-ML/DreamX-Creator for the native CPU runtime in rust-model-inference. DreamX-Creator generates synchronized video and audio from a first frame and a text prompt. These files also contain the optional 2x video refiner components. Both files are required and must be used together. The runtime verifies their shared pair ID before inference. Build rust-model-inference, then run: Add --dry-run to validate the model pair and inspect the memory estimate without running generation. Full inference produces base video/audio/mux outputs and, with --refine, refined video/mux outputs. The pair is exported from the original DreamX-Creator checkpoint with the repository's…

Model

drummer2-tools

Luke Steuber

This repository contains Tools 0.10.0, a narrow tool-proposal fine-tune of the 542M-parameter Drummer base at 10.85B pretraining tokens. It is trained for a fixed catalog of tools. It is a research model, not a general agent and not a product. - Three passes, 204 updates, 1,656,936 supervised target tokens; learning rate 1e-4. - The corpus is execution-verified against the tool executor. It teaches six local tools and four live web tools plus refusal/clarification behavior. Tool results are not model-written facts. The held-out result is the central limitation. Fine-tuned on the same rows, Qwen2.5-0.5B-Instruct made a valid call naming the unseen tool in 11/24 and SmolLM2-360M-Instruct in…

Model · Text generation

DualMind_Methodolgy

Convergent Intelligence

Roy C Abstract. We present a four-stage pipeline for producing small language models (1.7B parameters) that exhibit self-critiquing dual-cognition reasoning from a 30B-parameter teacher. The pipeline chains: (1) proof-weighted knowledge distillation from three teacher variants (Instruct, Thinking, Coder) of Qwen3-30B-A3B, each producing distinct capability profiles in the student; (2) Topological Knowledge Distillation (TKD), which uses the Discrepancy Calculus (DISC) framework to decompose the teacher's output distribution into smooth, jump, and singular-continuous components via bounded variation theory, allocating training capacity to structural boundaries that standard KD smears across…

Model · Text generation

duogpt-40m-v1

Duoia Pung

从零训练的 38.85M 参数英文小模型，消费级显卡（RTX 4060 Laptop 8GB）单卡训完。 本仓库同时提供了 GGUF 格式（由 model/litmodel.pth 直接转换，模板保持与训练一致），可直接用 Ollama 导入： scripts/chat.sh 里的三种问法： 在 Python 里直接用： - 只会做被交代的任务：给它一句裸问题（如 What is a volcano?）它不会回答；必须像上面那样把任务写清楚。 - 上下文上限 512 token：prompt + 生成长度之和；超出会报 Cannot forward sequence of length T。 - 预训练语料：TinyStoriesV2-GPT4（CDLA-Sharing-1.0）与 Children-Stories（用户自备，含 GPT 生成内容） - 本包模型权重由上述数据从零训练得到，仅供研究与个人使用；若再分发请一并保留此说明与 MODELCARD.md。

Model · Audio text to text

DuplexJev-B-Whisper-small-Qwen3-4B-2507

Adventists.ai

A small DuplexJev connector: the Whisper-small encoder (frozen) feeds a frozen Qwen3-4B-2507, and every typed question is read as a single-token distribution over its options, with zero decode steps and many questions per forward pass. Trained with the same recipe as the Qwen3-32B connectors released with the paper. Leaderboard scores (see the GitHub README): main language (mean of qa100, ZJU-ML, Easy-Turn) 53.5, paralinguistics (mean of gender, emotion) 43.0. Paper protocol: the evaluation prompts of the paper (single option order, one H200). duplexjev defaults: the package's own prompts with lang matched to the clip. Spoken QA with a small LLM is bounded by the LLM itself (see the…

Model · Audio text to text

DuplexJev-B-Whisper-small-SmolLM3-3B

Adventists.ai

A small DuplexJev connector: the Whisper-small encoder (frozen) feeds a frozen SmolLM3-3B, and every typed question is read as a single-token distribution over its options, with zero decode steps and many questions per forward pass. Trained with the same recipe as the Qwen3-32B connectors released with the paper. The connector was trained with SmolLM3's no-think format (an empty block before the answer). The tokenizer in this repository carries that chat template; SmolLM3's default template adds a long system prompt with today's date and would not match. Leaderboard scores (see the GitHub README): main language (mean of qa100, ZJU-ML, Easy-Turn) 37.4, paralinguistics (mean of gender…

static quants of https://huggingface.co/zime-ai/dwight-4b For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here…

The two pretrained models the EasyOCR en reader uses, converted from the official.pth files to.safetensors. Nothing was retrained or modified: every tensor is identical, in value and dtype, to the original, and the key names (including the module. prefix) are unchanged. SHA256SUMS lists the same values: sha256sum -c SHA256SUMS. Converted from the official EasyOCR release assets (EasyOCR's own MD5 values in easyocr/config.py are shown): Conversion: torch.load(..., weightsonly=True) of the state dict, then safetensors.torch.savefile. The saved file was loaded back and compared tensor by tensor with the original (all equal). An EasyOCR reader fed these tensors returned the same boxes, text and…

This is a frozen snapshot of CL, EDOS Engineering's experimental continual-learning chatbot, deployed under the persona "Hades." CL learns while it runs by editing the model's original quantized weight tensors in place. Its training is self-directed: the model selects its own training corpus, decides what is important, and is then trained on what it selected. It reads new AI research papers in full, judges which findings matter, writes the training text itself as attributed reviews, and marks which of them to learn and how strongly. It also learns from conversation. It uses no LoRA, adapter or side network. This export is the live model after 1,091 online learning updates, merged into one…

Model

efficientformer-checkpoint42

Chloé V. Wójcik

Working implementation of Efficientformer for Retrieval using a giant configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a linear warmup schedule. These are starting values in the script, not…

Model

efficientformer-matching-exp

V. Singh

Working implementation of Efficientformer for Matching using a base configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a polynomial schedule. These are starting values in the script, not evidence…

Model

efficientformer-retrieval-lab4

Alvinng05

This is an experimental Efficientformer codebase for Retrieval. It keeps the base setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses rmsprop with a onecycle schedule. These are starting values in the script, not evidence of a completed…

Model

efficientformer-retrieval-notes

Nikhilvnv

A small Efficientformer implementation for Retrieval, packaged with an explicit configuration and an initialization checkpoint. The nano variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses rmsprop with a linear warmup schedule. These are starting values in the…