Open-Weight Models Under Apache License 2.0

weighted/imatrix quants of https://huggingface.co/Ateron/Gemma-4-Writers-31B-V2 For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Gemma-4-Writers-31B-V2-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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 are Artefact2's thoughts on the…

This gemma2 model was trained 2x faster with Unsloth

This gemma2 model was trained 2x faster with Unsloth

weighted/imatrix quants of https://huggingface.co/ConicCat/Gemma4-Writer-31B-G For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Gemma4-Writer-31B-G-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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 are Artefact2's thoughts on the…

Model

generation

Michael Brown

This repository is a compact, custom PyTorch implementation of Beit for Generation. The nano configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained 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 adamw with a exponential schedule. These are…

Model

generation

Heitor Lopes

A small Mixer implementation for Generation, packaged with an explicit configuration and an initialization checkpoint. The base 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 lion with a cosine schedule. These are starting values in the script, not evidence of…

Model

generation

Vincent Lee

Working implementation of Perceiver for Generation 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 lion with a constant warmup schedule. These are starting values in the script, not evidence…

Model

generation-large

Jayden Smith

This repository is a compact, custom PyTorch implementation of Swin T for Generation. The large configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained 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 lion with a cosine schedule. These are…

Model

generation-medium-2024

Jacob

A small Mobilevit implementation for Generation, packaged with an explicit configuration and an initialization checkpoint. The xlarge 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 adafactor with a onecycle schedule. These are starting values in the script, not…

100x faster than generative LLMs • Runs on laptops & cloud CPUs • Global #1 on JevBench When you ask ChatGPT or Claude a question, it generates words one token at a time, like a person typing out an essay. That takes 2 to 5 seconds and burns expensive GPU compute. That is great for writing a story, but it is painfully slow and expensive for simple decisions: - "Did the AI make up this answer, or is it actually in the PDF?" - "Should this customer's message go to billing, shipping, or technical support?" - "Does the revenue bar chart support this financial claim?" - "Did the student get the math problem right according to the answer key?" Psychologist Daniel Kahneman described human thinking…

100x faster than generative LLMs • Runs on laptops & cloud CPUs • Global #1 on JevBench When you ask ChatGPT or Claude a question, it generates words one token at a time, like a person typing out an essay. That takes 2 to 5 seconds and burns expensive GPU compute. That is great for writing a story, but it is painfully slow and expensive for simple decisions: - "Did the AI make up this answer, or is it actually in the PDF?" - "Should this customer's message go to billing, shipping, or technical support?" - "Does the revenue bar chart support this financial claim?" - "Did the student get the math problem right according to the answer key?" Psychologist Daniel Kahneman described human thinking…

Model · Text to speech

ghana-tts-v2

Ghana NLP Community

End-to-end multi-lingual Variational Inference Text-to-Speech (VITS2) model covering 41 indigenous Ghanaian languages plus Ghanaian English. Checkpoints are automatically synced live from the NVIDIA H200 VM: - bertvits2checkpoints/: Current production Bert-VITS2 checkpoints (G.pth, D.pth, config.json). - v1autoregressivecheckpoints/: Previous discrete autoregressive experiment checkpoints.

Selected GLaDiT inference weights and the matching frozen UVP VGAE for initial-frame-conditioned generation of 64 future CylinderFlow frames. The source archive includes same-machine latency, Validation100 and plotting tools. 只需分享本仓库链接。DiT 权重及配套 VGAE 已公开；baseline 沿用既有已选权重和 prepared 数据，详见交接说明。 condition width512, 1000 training diffusion steps; DDIM eta0 sampling. updates and raw/EMA candidates, score0.08477181532805404 under the recorded 20-step, three independently scored sample protocol. - Parent training was paused at update811685 of the planned1000000 updates. This is the selected550k checkpoint, not the pause recovery checkpoint. - The accompanying VGAE was selected by its declared…

Model

glasseye-bbp-v1

CyberviserAI

GlassEye? authorized HackerOne Bug Bounty (BBP) / Vulnerability Disclosure (VDP) assistant. In-scope HackerOne program workflows: policy/scope, report writing, severity rationale, remediation. Not for unauthorized testing or exploit dump recipes.

Model

glasseye-bbp-v1

0ai

GlassEye? authorized HackerOne Bug Bounty (BBP) / Vulnerability Disclosure (VDP) assistant. In-scope HackerOne program workflows: policy/scope, report writing, severity rationale, remediation. Not for unauthorized testing or exploit dump recipes.

ONNX conversion of gliner-community/glinersmall-v2.5, dynamically quantised to INT8, packaged as a self-contained bundle for offline NER. This is a re-serialisation, not a fine-tune: the weights are the upstream ones. Only the format (PyTorch → ONNX) and the precision (fp32 → INT8) are ours. The fp32 reference graph (model.onnx, sha256 5245733ccb2b75072cce0b4bbb14424988f92f9daf775d97bdf0de74be28df63) is not shipped — it is only needed to reproduce the INT8 graph. Its hash is recorded in NOTICE. The graph has six inputs, fed per span-encoded prompt: spanmask is bool (not int64) — the graph declares tensor(bool). The prompt follows the GLiNER label format, using the model's own special tokens…

Model · Token classification

gliner25_agents_v3

Rafael Macalaba

Fine-tune of fastino/gliner2.5-base-v1 (GLiNER2, boundary architecture) for data-use mention extraction (dataset / survey / census / registry mentions in economics research papers). Labels were produced with a locally run LLM through the passage-annotation harness, with human-in-the-loop passage review and adjudication under the project doctrine. Holdout F1 is therefore agreement with the annotating agent, not owner accuracy. - NAMEDDATA — a proper name, title, or acronym of a specific data source - DESCRIPTIVEDATA — a source described in words but not named - VAGUEDATA — generic data wording with no identifiable source 2 gold mention(s) in the holdout sit inside a run the word splitter…

Core AI port of fastino/gliner2.5-multi-v1, the multilingual GLiNER2.5 boundary extractor. It is built on mDeBERTa-v3-base and has 287M parameters. It keeps the upstream decision behavior. All credit for the model goes to Fastino and the GLiNER2 authors. The original model card is included as - Checkpoint fastino/gliner2.5-multi-v1 at revision 12fc40399dae672ce840c5e3c50a92340bff3c8c. 55656fb (for preprocessing and decoding). - coreai-torch 0.4.3, coreai-core 1.0.0b3, Torch 2.11.0. - Learned weights are unchanged (FP16). The export only rewrites graph structure: - Constant DeBERTa relative-position buckets. The float log-bucket path produces wrong indices inside converted graphs. - A…

Model

GLM-5.3-Flash-Distill-Qwen-7B-LoRA

Sscl

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Model · Token classification

goby-9-ie-vi

Belumind

Joint entity + relation extraction for Vietnamese. The best-calibrated model in the goby family: higher precision at the same recall than its siblings. On clean text at each model own best threshold it ties goby-4 rather than beating it (see Measurement correction); on degraded text it is the worst of the family. Pick it for well-formed prose where you care about precision. Fine-tuned from knowledgator/gliner-relex-large-v1.0. Released by Belumind. Apache-2.0. Try it in the browser - no install, runs on ZeroGPU. goby-9 is not robust to degraded text - see Known limitations. That is not a small caveat, read it before choosing. Same training data as goby-4. The only difference is the loss…

Model · Text generation

gollem-v5-ckpts

Fabryka AI

Research checkpoints of sub-100M-parameter English language models, GPT-style decoders (nanoGPT lineage) trained for the The repository holds a controlled scaling study (tokens, width, optimizer) and a set of data-attribution experiments on the 64M model (paired continued-training runs that differ only in data). Every run folder holds plain PyTorch checkpoints (ckpt.pt or ckpt k.pt, where N is the training step in thousands). Folders with a README.md (all runs since 2026-09-25) contain the full specification: data pool and its hash, trainer version, seed, steps and tokens. "Result" is the checkpoint that the reported numbers come from; it is the planned last step (for the final runs, the…

static quants of https://huggingface.co/Lambent/Goose-7.2B-G1k-20261007 For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Goose-7.2B-G1k-20261007-i1-GGUF 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 are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9…

Model · Text to speech

Gooya-Microscopy-v2-exp

Reza Sayar

Self-contained experimental Persian text-to-speech: 2,221,495 parameters, including G2P, a contextual GrainSpeech acoustic model, and a tiny speaker-adapted HiFi-GAN vocoder. Produces 24 kHz mono WAV audio. The entire stack is included in this repository and runs offline after downloading and installing dependencies. This is the stack used for the September 24 listening comparison. It includes the newer broad-corpus 837K student selected at epoch 16, not the earlier Nasle-only student. No Vocos, AvaSanj, training corpus, external checkpoint, Lightning, or remote inference service is required. Python 3.10+; CPU is the default. CUDA is optional. PyTorch is a separate runtime dependency: the…

Model · Text to speech

Gooya-RizehPizeh-v2-exp

Reza Sayar

Self-contained experimental Persian text-to-speech: 9,451,575 parameters, including G2P, a contextual GrainSpeech acoustic model, and a tiny speaker-adapted HiFi-GAN vocoder. Produces 24 kHz mono WAV audio. The entire stack is included in this repository and runs offline after downloading and installing dependencies. This is the stack used for the September 24 listening comparison. It includes the full 8.07M Negara v7.1 model and its overlay. No Vocos, AvaSanj, training corpus, external checkpoint, Lightning, or remote inference service is required. Python 3.10+; CPU is the default. CUDA is optional. PyTorch is a separate runtime dependency: the model weight size is not the installation…

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 3 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2

Model · Text generation

gpt-oss-120b-blackhole-fast

Jashan Pratap Singh

gpt-oss-120b served with vLLM on four Blackhole chips (a QuietBox 2, P150x4, tensor parallel 4). A lone request decodes through a fused batch-1 decode: DRAM-streaming expert matmuls that read the 128 BFP4 experts in place (one ND-sharded copy shared with prefill and the batched decode), on-core top-4 routing over the 128 router logits, and all-reduces fused into the producing kernels. Batches of 2-32, prefill, prefix caching and requests that need penalties, log-probs, top-k > 32, logit bias or grammars run the batched paths. Status: round-2 release candidate (stage 14): measured on p150x4 against tt-hous/gpt-oss-120b-p150x4 on the same day: c=1 decode 3.8x, GSM8K-250 and IFEval-100 above…

Model · Text generation

gpt-oss-20b-blackhole-fast

Jashan Pratap Singh

Runs on p150 (one Blackhole P150 chip), p150x2 (the two P150 ASICs of one P300C board) and p150x4 (the four P150 ASICs of two P300C boards, a QuietBox 2). Same model, same serving stack and the same six profiles as tt-hous/gpt-oss-20b-p150, with faster decode: a lone request on P150x4, P150x2, P150 decodes through fused, DRAM-streaming decode ops (from tenstorrent/tt-metal#60161, adapted to this runtime and extended to one and two chips). On P150x4, 2 to 4 decoding requests share one pass of those ops, and 5 to 32 run the base package's batched decode with the routed experts streamed from the fused path's weight copies. Prefill takes the base package's code path with faster expert dispatch…

This model is a fine-tuned version of Qwen/Qwen3-0.6B on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 4 - evalbatchsize: 8 - gradientaccumulationsteps: 16 - totaltrainbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 100 - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.23.2

GGUF build of openai/gpt-oss-20b for llama.cpp / Ollama / LM Studio. Converted by axteam with llama.cpp. The chat template (OpenAI harmony format) is embedded in the GGUF. Use a recent llama.cpp build (gpt-oss support, Aug-2025 or later). Reasoning effort can be set in the system prompt (Reasoning: low|medium|high). KV-cache type is a run-time choice in llama.cpp (not baked into the file): Apache-2.0, following the original openai/gpt-oss-20b. Weights, tokenizer, and chat template belong to OpenAI. This repo only re-packages them as GGUF. openai/gpt-oss-20b 의 GGUF 빌드 (llama.cpp·Ollama·LM Studio용). 변환: axteam (llama.cpp, chattemplate(OpenAI harmony)은 GGUF에 내장). - 파일…

This is the lab4-yoru-AY-482206 checkpoint, trained from scratch on English C4. It was selected by the lowest development loss among nine experimental recipes. The independent seed repeat and reserved audit evaluation were still pending when this checkpoint was published. Reported scores are local evaluation proxies, not an official online-judge result. - Stock Hugging Face GPT2LMHeadModel: 12 layers, 12 attention heads, hidden width 768, context length 1,024, vocabulary 50,257, tied input/output embeddings. - 124,439,808 unique parameters, commonly described as GPT-2 small. The lab uses the historical 117M model-family label. - GPT-2 tokenizer; documents packed with EOS separators. No…

Fine-tuned GR00T-N1.5 victim policy trained on sequentially perturbed LIBERO-10 demonstrations.

Model · Text generation

granite-4.0-h-small-w4a16-llmcompressor

AMD

ZenDNN v6.1.0 - ZenTorch v2.13.0.0 - PyTorch v2.13.0.0 - LLM Compressor v0.13.0 - vLLM v0.29.0 This is a quantized version of granite-4.0-h-small created by AMD using LLM Compressor (compressed-tensors) for ZenDNN-optimized CPU inference. The model was quantized from granite-4.0-h-small using LLM Compressor with the GPTQ algorithm. This reduces the model weights from 60.0 GiB to 16.1 GiB on disk (~73% reduction). granite-4.0-h-small is a hybrid Mamba-MoE model: of its 40 layers, 4 are full-attention blocks and the other 36 are Mamba (linear-attention) blocks, and every layer carries a 72-expert MoE block (top-10 routing) alongside a shared MLP. Two details make this work. The model is…

Model · Text generation

granite-4.0-h-small-w8a8-llmcompressor

AMD

ZenDNN v6.1.0 - ZenTorch v2.13.0.0 - PyTorch v2.13.0.0 - LLM Compressor v0.13.0 - vLLM v0.29.0 This is a quantized version of granite-4.0-h-small created by AMD using LLM Compressor (compressed-tensors) for ZenDNN-optimized CPU inference. The model was quantized from granite-4.0-h-small using LLM Compressor via the Round-to-Nearest (RTN) algorithm. This reduces the model weights from 60.0 GiB to 30.4 GiB on disk (~49% reduction). granite-4.0-h-small is a hybrid Mamba-MoE model: of its 40 layers, 4 are full-attention blocks and the other 36 are Mamba (linear-attention) blocks, and every layer carries a 72-expert MoE block (top-10 routing) alongside a shared MLP. The recipe only needs two…

Model · Text generation

granite-4.0-h-tiny-w8a8-llmcompressor

AMD

ZenDNN v6.1.0 - ZenTorch v2.13.0.0 - PyTorch v2.13.0.0 - LLM Compressor v0.13.0 - vLLM v0.29.0 This is a quantized version of granite-4.0-h-tiny created by AMD using LLM Compressor (compressed-tensors) for ZenDNN-optimized CPU inference. The model was quantized from granite-4.0-h-tiny using LLM Compressor via the Round-to-Nearest (RTN) algorithm. This reduces the model weights from 12.9 GiB to 6.6 GiB on disk (~49% reduction). granite-4.0-h-tiny is a hybrid Mamba-MoE model: of its 40 layers, 4 are full-attention blocks and the other 36 are Mamba (linear-attention) blocks, and every layer carries a 64-expert MoE block alongside a shared MLP. The recipe only needs two ignore entries. lmhead…

Model · Text generation

Granite-4.2-30B-xVITA-zhTW-Uncensored-GGUF

xCloudinfo Corp.Limited

以 Granite-4.2-30B-xVITA-zhTW（台灣繁中微調版）為來源，不是重新訓練，而是在權重上做一次正交化，把模型用來決定「要不要拒絕」的方向投影掉。 - 類型：因果語言模型，dense 29.3B、64 層 - 來源模型：xCloudinfo/Granite-4.2-30B-xVITA-zhTW（底座 ibm-granite/granite-4.2-30b） - 製作方式：單方向拒絕消除（weight orthogonalization，Arditi et al. 2024），非微調、非重訓 - 授權：Apache-2.0（沿用 Granite 底座授權） - 發布格式：GGUF（六階量化） 結論：去審查沒有顯著損及一般能力。 台灣口語行文風格完整保留（文體分還略高）；知識與程式各少答一題，落在小題庫雜訊範圍。可視為「開放版的 xVITA」。 本模型是云碩 xVITA 台灣繁中語音 RAG 套件組的生成＋工具層。完整迴圈（每一環都是自訓的繁中模型）： 語音聽寫（Whisper-v3-Turbo-xVITA）→ 語意檢索（xVITA-Embed）→ 重排序（xVITA-Rerank）→ 本模型：生成＋工具呼叫（看圖走 Gemma-4 xReadable OCR）→ 台灣腔語音合成（xVITA-TTS）。 答案踩在檢索到的台灣文件上重寫，台灣口語行文在 RAG 裡尤其加分（讀起來像台灣人寫的、不漂中國用語、不寫 AI 報告體）；整套可全地端私有化，適合政府與企業內部。生成層換成本開放版，還能處理被過度拒絕擋下的內部／資安問答。 IQ 階以校準語料計算…

Model · Text generation

granite-4.2-3b-MXFP4A16-GPTQ

David Reedy

An experimental MXFP4 quantization of ibm-granite/granite-4.2-3b, made with GPTQ calibration instead of the usual data-free rounding, and provided in two formats with identical weights. It's published as a data point, not as the recommended build. For most users, the Q4KM GGUF or GPTQ int4 build is the better choice. Format: OCP MX — E2M1 (FP4) weights, groupsize=32, one UE8M0 shared scale per group; weights only, activations stay 16-bit. Embeddings, norms and lmhead are unquantized. MXFP4 is usually produced data-free, by rounding weights straight to FP4 with no calibration data. llm-compressor ships an MXFP4A16 scheme, but every example we found pairs GPTQModifier with integer schemes…

Model

granite-4.2-3b-oQ8e

Michael

This model was quantized using oQ (oMLX v0.7.0) mixed-precision quantization.

Model · Text generation

granite-4.2-3b-Q4_K_M-imatrix-GGUF

David Reedy

A 4-bit GGUF of ibm-granite/granite-4.2-3b, quantized with an importance matrix built from a mixed prose and tool-calling calibration corpus. Of the three quantizations we made of this model, it's the closest to the original and the smallest. "Same top token" is llama.cpp's Same top p statistic: the share of positions where the quantized model's most likely next token matches the f16 model's. 1. Convert: converthftogguf.py (mainline llama.cpp), bf16 safetensors → f16 GGUF. Granite's scaling parameters (attentionscale, embeddingscale, residualscale, logitsscale) are supported natively. 2. Importance matrix: llama-imatrix over prose plus tool-calling conversations, rendered through Granite's…

Model · Text generation

granite-4.2-3b-W4A16-GPTQ-gs32

David Reedy

A 4-bit weight-only GPTQ quantization of ibm-granite/granite-4.2-3b, saved in compressed-tensors format for vLLM. On an AMD Radeon Pro V620 (RDNA2), vLLM served it at 65 tok/s single-sequence and ~1,000 tok/s aggregate at 16 concurrent sequences. llm-compressor GPTQModifier: - int4, symmetric, weight-only (activations stay 16-bit) - groupsize=32, actorder="weight" - lmhead left unquantized 512 samples, maxseqlength=512 The tuning mattered. With the default W4A16 preset (groupsize=128), the same pipeline scored 0.153 mean KLD. Moving to groupsize=32 with actorder="weight" cut that by about 28%, for roughly 0.2 GB more disk. For comparison, an AWQ build (W4A16 asymmetric, groupsize=128) from…

Model

granite-4.2-8b-oQ6e

Michael

This model was quantized using oQ (oMLX v0.7.0) mixed-precision quantization.

Model · Speech recognition

granite-speech-4.1-2b-nar-glade

Josh Leverette

Glade is a native Swift library for on-device speech inference on Apple silicon. This repository supplies converted model assets and configuration for Glade's model-specific host runtime. Glade's runtime source is currently private. A community conversion of IBM Granite Speech 4.1 2B NAR for Glade, using Apple's Core AI APIs and optimized for Neural Engine execution on Apple silicon. The model combines a CTC acoustic encoder with one bidirectional, audio-conditioned transcript-editing pass. This is a W8A16 export: most convolution/projection weights are INT8 and activations are FP16. Conformer convolution output projections, normalization parameters, and the embedding table remain FP16.…

Model · Speech recognition

granite-speech-5.0-470m-turboctc-glade

Josh Leverette

Glade is a native Swift library for on-device speech inference on Apple silicon. This repository supplies converted model assets and configuration for Glade's model-specific host runtime. Glade's runtime source is currently private. Glade source asset for IBM's English CTC speech recognizer, pinned to The upstream model and these converted weights use Apache License 2.0. Requires physical Apple silicon with macOS 27 or iOS 27 and Glade's host runtime. This is a source.aimodel, which Core AI specializes on the device. It is not a precompiled.aimodelc or a standalone transcription application. One static entry point, frames3072, handles up to 3,072 feature positions at approximately 50…

This is a merged QLoRA derivative of Qwen/Qwen3-4B-Instruct-2507, published for Headroom, developed under the working name Greenlight. The original model identifier is retained for reproducibility. Training used 336 synthetic examples and 300 configured optimizer steps through Veri. Dataset SHA-256: ed827924d7ba4118fa15c5ef71fb211c557c3390d87384f21af4eb86ae59d051. Training job: 42d582a35d2f. Rank 8, 4-bit loading, learning rate 0.0001, sequence limit 2048, micro-batch 1, gradient accumulation 2. Prompt profile: reference-contract-v5. This model proposes bounded structured payment terms. Consult the repository’s matched frozen base/tuned evaluation before making any quality claim. The corpus…

Model · Robotics

groot-n17-droid-pour-water-10ep

Sang Nguyen

GR00T N1.7 is an open, cross-embodiment foundation model from NVIDIA for generalized humanoid robot reasoning and skills. It uses a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer to predict actions conditioned on vision, language, and proprioception. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot groot 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…

GR00T-N1.5-3B fine-tuned with LeRobot (0.4.1) on the 379 LIBERO-10 (LIBERO-Long) demonstrations, on a single RTX 4090. This is the final 60,000-step checkpoint. It is a color-jitter ablation of an RGB-vs-grayscale study. The recipe is identical to except that training-time color jitter is turned off. It reaches 66% on LIBERO-10 under default lighting. It is much less robust to lighting changes than the jittered RGB and grayscale models. zhoumiaosen/libero10rgb: the 379 LIBERO-10 episodes of openvla/modifiedliberorlds, converted to LeRobot v3.0 at 10 fps. That is 101,469 frames. observation.state (8-D: eef position, axis-angle, 2 gripper joints). Output: 7-D action (eef delta + gripper).…

Model

grouptok-en-sw-af-de-fr-es-zh-152k

Henry Hale

A byte-level BPE tokenizer of 151,665 tokens for English, Swahili, Afrikaans, German, French, Spanish and Chinese, whose vocabulary is grouped by translation with grouptok: a group gathers tokens that translate each other, across languages (e.g. water, Wasser, eau, agua, maji). Its tokenizer.json is exactly that of the baseline bpe-en-sw-af-de-fr-es-zh-152k, so the two split every text identically and differ only in groups.json. No pad, bos or eos token is declared; set them for your model if you need them. Each sentence pair is split by the BPE, and its language's aligner links the English subwords to the other side's. The links of all pairs and all languages are pooled and turned into…

Model

grouptok-en-sw-af-de-fr-es-zh-200k

Henry Hale

A byte-level BPE tokenizer of 200,000 tokens for English, Swahili, Afrikaans, German, French, Spanish and Chinese, whose vocabulary is grouped by translation with grouptok: a group gathers tokens that translate each other, across languages (e.g. water, Wasser, eau, agua, maji). Its tokenizer.json is exactly that of the baseline bpe-en-sw-af-de-fr-es-zh-200k, so the two split every text identically and differ only in groups.json. No pad, bos or eos token is declared; set them for your model if you need them. Each sentence pair is split by the BPE, and its language's aligner links the English subwords to the other side's. The links of all pairs and all languages are pooled and turned into…

Qwen/Qwen3.5-9B trained with grpo on the seven-task setting: 100 steps, 32 prompts x 8 rollouts per step, lr 2e-6, KL 1e-3, 32K rollout cap, verl v0.9.1 (FSDP2 + vLLM). Training data: 512 examples (seed 0) from the official training splits; system prompt "Solve the user's task and give the final answer directly."; reward = task correctness of the text after.

EfficientNetV2-L (torchvision efficientnetv2l) fine-tuned for the Perturb subnet (netuid 26).

EfficientNetV2-L (torchvision efficientnetv2l) fine-tuned for the Perturb subnet (netuid 26).