# Models, Page 421 of 464 Source: https://savrn.com/models/page/421 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub # Open-Weight Models An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data. Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page. 11,122Models 3,164Datasets 285Papers 6,792Publishers 11,852Sourced relationships Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,362](https://savrn.com/models?task=Text+generation) - [Image and text to text494](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction332](https://savrn.com/models?task=Feature+extraction) - [Robotics329](https://savrn.com/models?task=Robotics) - [Text classification293](https://savrn.com/models?task=Text+classification) - [Text to image202](https://savrn.com/models?task=Text+to+image) - [Speech recognition170](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification135](https://savrn.com/models?task=Image+classification) - [Text to speech130](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity102](https://savrn.com/models?task=Sentence+similarity) - [Token classification96](https://savrn.com/models?task=Token+classification) - [Zero-shot classification92](https://savrn.com/models?task=Zero-shot+classification) - [Reinforcement learning91](https://savrn.com/models?task=Reinforcement+learning) - [Image segmentation87](https://savrn.com/models?task=Image+segmentation) - [Object detection86](https://savrn.com/models?task=Object+detection) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [Translation77](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask67](https://savrn.com/models?task=Fill+mask) - [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Image to text57](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,718](https://savrn.com/models?library=transformers) - [peft329](https://savrn.com/models?library=peft) - [diffusers183](https://savrn.com/models?library=diffusers) - [pytorch169](https://savrn.com/models?library=pytorch) - [gguf165](https://savrn.com/models?library=gguf) - [sentence-transformers138](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [lerobot135](https://savrn.com/models?library=lerobot) - [timm49](https://savrn.com/models?library=timm) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [llama.cpp40](https://savrn.com/models?library=llama.cpp) - [vllm38](https://savrn.com/models?library=vllm) - [onnx35](https://savrn.com/models?library=onnx) - [diffusion-single-file27](https://savrn.com/models?library=diffusion-single-file) - [minimax-h324](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino18](https://savrn.com/models?library=openvino) - [coreml17](https://savrn.com/models?library=coreml) - [openpi16](https://savrn.com/models?library=openpi) - [exllamav315](https://savrn.com/models?library=exllamav3) - [stable-baselines315](https://savrn.com/models?library=stable-baselines3) - [keras14](https://savrn.com/models?library=keras) Modality - [Other6,276](https://savrn.com/models?modality=Other) - [Text2,536](https://savrn.com/models?modality=Text) - [Image554](https://savrn.com/models?modality=Image) - [Image and text551](https://savrn.com/models?modality=Image+and+text) - [Audio473](https://savrn.com/models?modality=Audio) - [Control420](https://savrn.com/models?modality=Control) - [Video114](https://savrn.com/models?modality=Video) - [Multimodal79](https://savrn.com/models?modality=Multimodal) - [Time series64](https://savrn.com/models?modality=Time+series) - [Tabular55](https://savrn.com/models?modality=Tabular) License - [apache-2.03,031](https://savrn.com/models?license=apache-2.0) - [mit1,221](https://savrn.com/models?license=mit) - [other681](https://savrn.com/models?license=other) - [cc-by-nc-4.0184](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0147](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma59](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.234](https://savrn.com/models?license=llama3.2) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [llama3.127](https://savrn.com/models?license=llama3.1) - [unknown26](https://savrn.com/models?license=unknown) - [cc-by-sa-4.018](https://savrn.com/models?license=cc-by-sa-4.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [gpl-3.016](https://savrn.com/models?license=gpl-3.0) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [openrail13](https://savrn.com/models?license=openrail) - [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m) - [cc-by-nc-nd-4.010](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama26](https://savrn.com/models?license=llama2) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights10,787](https://savrn.com/models?access=open_access) - [Access requested at publisher335](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher286](https://savrn.com/models?publisher=mradermacher) - [Qwen91](https://savrn.com/models?publisher=qwen) - [RunningHub.ai91](https://savrn.com/models?publisher=runninghubai) - [Yurun Yuan86](https://savrn.com/models?publisher=ryanyr) - [Jenny Wilson81](https://savrn.com/models?publisher=jenny08311) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [Jessen Pu69](https://savrn.com/models?publisher=putty77) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan61](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami60](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [Yurun Yuan44](https://savrn.com/models?publisher=yuruny) - [BAAR43](https://savrn.com/models?publisher=aixk) - [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud) [Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params) 11,122 models, sorted by most downloaded. Model · Mask generation ### [slimsam-77-uniform-boxes](https://savrn.com/models/slimsam-77-uniform-boxes) [Anirudh Aravalli](https://savrn.com/model-publishers/aranirudh) SlimSAM (nielsr/slimsam-77-uniform) in ONNX for transformers.js, like Xenova/slimsam-77-uniform — whose image encoder files are reused here unchanged — but with a prompt encoder / mask decoder that takes box prompts. Xenova/slimsam-77-uniform's decoder declares no inputboxes, and transformers.js drops inputs a session does not declare, so a box passed to it is silently ignored. This decoder follows Meta's own SAM ONNX convention instead: a box is two extra points in inputpoints, labelled 2 (top-left corner) and 3 (bottom-right corner). Inputs and outputs keep transformers.js's names, so SamModel / SamProcessor drive it unchanged. One rule the caller must follow: a prompt without a box… Open weights apache-2.0 transformers.js [View model](https://savrn.com/models/slimsam-77-uniform-boxes) Model · Video to video ### [slither-wam-pretrain](https://savrn.com/models/slither-wam-pretrain) [Seth Lupo](https://savrn.com/model-publishers/mundoamundo) The six-H200 full-corpus run is in progress. Its rolling latest checkpoint is in a mutable Storage Bucket, with the verified step recorded in latest.json. The final weights will be published in this model repository. Live training and held-out validation metrics are updated throughout the run. The model initializes from OpenWAM-α at revision 52df4e66c82c5c8b480adcc8d01f4db7415dfb56. Its Slither adapter replaces the 80-dimensional robot action head with three outputs: cosine and sine of estimated mouse angle, and binary boost. It removes robot proprioception and uses fixed empty-prompt conditioning. Each example uses 33 frames at 30 Hz and 32 transition actions; the nine video frames are… Open weights openwam [View model](https://savrn.com/models/slither-wam-pretrain) Model ### [slither-wan-v3-20261001](https://savrn.com/models/slither-wan-v3-20261001) [Seth Lupo](https://savrn.com/model-publishers/mundoamundo) 1,427,969,472 trainable parameters, initialized from pinned Wan2.1-T2V-1.3B. Frozen native video codec, 512×288 at 15 Hz, three seconds observed history, 30 Hz proxy angle/boost controls. Joint stochastic future blocks with native sigma flow matching. Policy fitting is a later separate stage. Resumable latest and best checkpoints: https://huggingface.co/buckets/mundoamundo/slither-wan-v3-20261001. Read latest.json or best.json for verified file pointers, sizes, and SHA-256. Two rotating slots per kind; no repeated weight commits to this Git repository. Checkpoints include model, optimizer, data cursor, and per-rank RNG. Dataset: mundoamundo/slither-wam-video-actions at… Open weights apache-2.0 [View model](https://savrn.com/models/slither-wan-v3-20261001) Model · Text generation ### [slm-125m-base](https://savrn.com/models/slm-125m-base) [Tijani Ohiokpehai](https://savrn.com/model-publishers/tohio) tohio/slm-125m-base is a small language model (125M parameters) built from scratch and aligned end-to-end using the slm-gpt engine. - Decoder-Only Transformer with Rotary Position Embeddings (RoPE) The base model was pre-trained across an interleaved multi-source domain mixture composed of: - FineWeb-Edu - DCLM-Edu - The Stack-Edu - NuminaMath-CoT - OpenMathReasoning - SLM-Synthetic-Pretrain 1. Pre-training: Multi-GPU Distributed Data Parallel (DDP) over rank-disjoint memory-mapped token shards. 2. Supervised Fine-Tuning (SFT): Full parameter instruction-tuning on ChatML formatted dialogues with prompt loss masking (ignoreindex=-100). 3. Direct Preference Optimization (DPO): Single-stage… Open weights mit 165M parameters 2,048 tokens transformers [View model](https://savrn.com/models/slm-125m-base) Model · Text generation ### [slm-125m-dpo](https://savrn.com/models/slm-125m-dpo) [Tijani Ohiokpehai](https://savrn.com/model-publishers/tohio) tohio/slm-125m-dpo is a small language model (125M parameters) built from scratch and aligned end-to-end using the slm-gpt engine. - Decoder-Only Transformer with Rotary Position Embeddings (RoPE) The base model was pre-trained across an interleaved multi-source domain mixture composed of: - FineWeb-Edu - DCLM-Edu - The Stack-Edu - NuminaMath-CoT - OpenMathReasoning - SLM-Synthetic-Pretrain 1. Pre-training: Multi-GPU Distributed Data Parallel (DDP) over rank-disjoint memory-mapped token shards. 2. Supervised Fine-Tuning (SFT): Full parameter instruction-tuning on ChatML formatted dialogues with prompt loss masking (ignoreindex=-100). 3. Direct Preference Optimization (DPO): Single-stage… Open weights mit 165M parameters 1,024 tokens transformers [View model](https://savrn.com/models/slm-125m-dpo) Model · Text generation ### [slm-125m-sft](https://savrn.com/models/slm-125m-sft) [Tijani Ohiokpehai](https://savrn.com/model-publishers/tohio) tohio/slm-125m-sft is a small language model (125M parameters) built from scratch and aligned end-to-end using the slm-gpt engine. - Decoder-Only Transformer with Rotary Position Embeddings (RoPE) The base model was pre-trained across an interleaved multi-source domain mixture composed of: - FineWeb-Edu - DCLM-Edu - The Stack-Edu - NuminaMath-CoT - OpenMathReasoning - SLM-Synthetic-Pretrain 1. Pre-training: Multi-GPU Distributed Data Parallel (DDP) over rank-disjoint memory-mapped token shards. 2. Supervised Fine-Tuning (SFT): Full parameter instruction-tuning on ChatML formatted dialogues with prompt loss masking (ignoreindex=-100). 3. Direct Preference Optimization (DPO): Single-stage… Open weights mit 165M parameters 1,024 tokens transformers [View model](https://savrn.com/models/slm-125m-sft) Model ### [slop-puker-qnn](https://savrn.com/models/slop-puker-qnn) [J S](https://savrn.com/model-publishers/sloppilysloppy) Open weights [View model](https://savrn.com/models/slop-puker-qnn) Model ### [slopblock-modernbert-base](https://savrn.com/models/slopblock-modernbert-base) [Dang](https://savrn.com/model-publishers/salmonfamin) Open weights 8,192 tokens [View model](https://savrn.com/models/slopblock-modernbert-base) Model ### [sm70-fp8-block32-kernels](https://savrn.com/models/sm70-fp8-block32-kernels) [Nick Mermiges](https://savrn.com/model-publishers/proprietarylegal) These are weight-only FP8 kernels for NVIDIA Volta (V100, SM70). They serve checkpoints whose weights are E4M3 with one power-of-two (UE8M0) scale per 32 × 32 block, the dense format of DeepSeek-V4.1-Flash (weightblocksize: [32, 32], scalefmt: ue8m0). Volta has no FP8 tensor cores, so the weights stay FP8 in memory and are expanded to FP16 inside the kernels. Weight memory falls from 2 bytes per parameter (FP16 dequantization) to 1.0625 bytes. For DeepSeek-V4.1-Flash's per-layer dense set at TP4, decode-size products (1–16 rows) run 9–36% faster than FP16 weights with cuBLAS. Individual projections vary (see Results). This repository is a distribution of a patch series for 1Cat-vLLM, the… Open weights apache-2.0 vllm [View model](https://savrn.com/models/sm70-fp8-block32-kernels) Model ### [smallprint-2b](https://savrn.com/models/smallprint-2b) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E2B at revision d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.2% (94.7% to 95.5%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 5.1B parameters 131,072 tokens [View model](https://savrn.com/models/smallprint-2b) Model ### [smallprint-2b-GGUF](https://savrn.com/models/smallprint-2b-gguf-2) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/Peter-A-P/smallprint-2b 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… Open weights apache-2.0 transformers [View model](https://savrn.com/models/smallprint-2b-gguf-2) Model ### [smallprint-2b-gguf](https://savrn.com/models/smallprint-2b-gguf) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E2B at revision d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.2% (94.7% to 95.5%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 [View model](https://savrn.com/models/smallprint-2b-gguf) Model ### [smallprint-2b-lora](https://savrn.com/models/smallprint-2b-lora) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E2B at revision d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.2% (94.7% to 95.5%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 [View model](https://savrn.com/models/smallprint-2b-lora) Model ### [smallprint-4b](https://savrn.com/models/smallprint-4b) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E4B at revision 411aa17b749aa952df1359d2dcea73917a544d9a, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.1% (94.7% to 95.4%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 7.9B parameters 131,072 tokens [View model](https://savrn.com/models/smallprint-4b) Model ### [smallprint-4b-gguf](https://savrn.com/models/smallprint-4b-gguf) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E4B at revision 411aa17b749aa952df1359d2dcea73917a544d9a, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.1% (94.7% to 95.4%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 [View model](https://savrn.com/models/smallprint-4b-gguf) Model ### [smallprint-4b-lora](https://savrn.com/models/smallprint-4b-lora) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E4B at revision 411aa17b749aa952df1359d2dcea73917a544d9a, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.1% (94.7% to 95.4%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 [View model](https://savrn.com/models/smallprint-4b-lora) Model ### [smallprint-7b](https://savrn.com/models/smallprint-7b) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 7.3B parameters 65,536 tokens [View model](https://savrn.com/models/smallprint-7b) Model ### [smallprint-7b-GGUF](https://savrn.com/models/smallprint-7b-gguf-2) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/Peter-A-P/smallprint-7b 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… Open weights apache-2.0 transformers [View model](https://savrn.com/models/smallprint-7b-gguf-2) Model ### [smallprint-7b-awq](https://savrn.com/models/smallprint-7b-awq) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 7.3B parameters 65,536 tokens [View model](https://savrn.com/models/smallprint-7b-awq) Model ### [smallprint-7b-gguf](https://savrn.com/models/smallprint-7b-gguf) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 [View model](https://savrn.com/models/smallprint-7b-gguf) Model ### [smallprint-7b-gptq](https://savrn.com/models/smallprint-7b-gptq) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 7.3B parameters 65,536 tokens [View model](https://savrn.com/models/smallprint-7b-gptq) Model ### [smallprint-7b-lora](https://savrn.com/models/smallprint-7b-lora) [Peter Parker](https://savrn.com/model-publishers/peter-a-p) Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the… Open weights apache-2.0 [View model](https://savrn.com/models/smallprint-7b-lora) Model · Robotics ### [smolVLA_cube_training_EE_1.1](https://savrn.com/models/smolvla-cube-training-ee-1-1) [Audrey Boardman](https://savrn.com/model-publishers/aboardman) SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint. Open weights apache-2.0 450M parameters lerobot [View model](https://savrn.com/models/smolvla-cube-training-ee-1-1) Model · Robotics ### [smolvla](https://savrn.com/models/smolvla) [Sathyaraj Medipalli](https://savrn.com/model-publishers/sathyarajmedipalli) SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI… Open weights apache-2.0 450M parameters lerobot [View model](https://savrn.com/models/smolvla) ## Model Collections Hand-picked starting points, each with the reason it exists. Collection · 4 entries ### [Embedding models for retrieval](https://savrn.com/collections/embedding-models-for-retrieval) Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher. [View collection](https://savrn.com/collections/embedding-models-for-retrieval) Collection · 4 entries ### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-on-one-accelerator) Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer. [View collection](https://savrn.com/collections/models-that-run-on-one-accelerator) Collection · 6 entries ### [Open-weight text models worth knowing](https://savrn.com/collections/open-weight-text-models-worth-knowing) Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking. [View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing) Collection · 3 entries ### [Speech and audio models](https://savrn.com/collections/speech-and-audio) Recognition and synthesis models, grouped so the two directions are easy to compare. [View collection](https://savrn.com/collections/speech-and-audio) ## Open-Weight Models Explained ### What is an open-weight model? An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls. ### Is an open-weight model the same as open source? Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only. ### Can I use an open-weight model commercially? It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license. ### How much memory does an open-weight model need? About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files. ## Related SAVRN Research The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them. SAVRN Index ### [What open models cost to run](https://savrn.com/ai-index/pricing/open-models) The same open-weight model priced by every host that serves it, per million tokens. Research Hub ### [Data center trackers and maps](https://savrn.com/data-center-research) Moratoriums, permits, power, water and capital behind the facilities that run these models. Method ### [How the Model Hub is built](https://savrn.com/model-hub/methodology) Sources, evidence labels, refresh behaviour, and the limits of every comparison here.