An independently trained bilingual model for ranking compiler-owned Gooo structural paths. The workflow is natural-language judgment → typed path ranking → finite tests → code assembly → recorded feedback. First proposal accuracy, finite completion, remaining candidates, failed cases and cost are reported separately. Three training comparisons each include FP32, post-training ternary and ternary-aware variants. No Laya weights are used. Two comparisons transfer the hidden layer of our own public operator model; positioned-random is independently initialized. Runtime, data generation, assembly and evaluation are Go. Python and PyTorch are used only for offline MPS training. Five edits: local…
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.
11,734Models
3,323Datasets
288Papers
7,150Publishers
12,282Sourced relationships
Updated 2026-10-20 · How the library is built
11,734 models, sorted by most downloaded.
GPROF IR is a satellite precipitation retrieval for geostationary IR observations. - 11 um brightness temperatures from geostationary sensors For questions see corresponding author in reference.
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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
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…
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…
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
The released GPT-2 large weights (774M parameters) fine-tuned into a chat model on with the loss on assistant replies only. Small, medium and large versions are compared in the instruction-tuning write-up. What is the capital of France? - best.safetensors: the weights at the lowest validation loss (the final step). - config.json: the training config. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers, and a vocabulary of 50,263 tokens: GPT-2's plus six chat tokens. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository generatetext samples with temperature 1 and top-5 by default. The system message is optional. The…
The released GPT-2 medium weights (355M parameters) fine-tuned into a chat model on with the loss on assistant replies only. Small, medium and large versions are compared in the instruction-tuning write-up. What is the capital of France? - best.safetensors: the weights at the lowest validation loss (the final step). - config.json: the training config. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers, and a vocabulary of 50,263 tokens: GPT-2's plus six chat tokens. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository generatetext samples with temperature 1 and top-5 by default. The system message is optional. The…
The released GPT-2 small weights fine-tuned to classify AG News articles into four topics: World, Sports, Business and Sci/Tech. Four setups compare how much of the model needs to train. Training details and plots: Each setup trained for 3 epochs on one RTX 2080. Test numbers come from the checkpoint with the lowest validation loss. Each folder holds best.safetensors and its training config.json. logs/ has the training output of each run. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository The classifier reads the logits at each input's last real token. Inputs were…
GPT-2 small (124M parameters) trained from random initialisation on the FineWeb-Edu sample-10BT sample, with the model and training loop written directly in PyTorch. Training details and plots: - gpt2step018890.safetensors: the final weights. - config.json: the training config. - checkpoints/step.pt: the last three resume checkpoints, with optimizer and RNG state, for continuing training with pretraining/train.py --resume. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository It was trained on educational web text in English only and can produce factually wrong or…
The released GPT-2 small weights (124M parameters) fine-tuned into a chat model on with the loss on assistant replies only. Small, medium and large versions are compared in the instruction-tuning write-up. What is the capital of France? - best.safetensors: the weights at the lowest validation loss (the final step). - config.json: the training config. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers, and a vocabulary of 50,263 tokens: GPT-2's plus six chat tokens. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository generatetext samples with temperature 1 and top-5 by default. The system message is optional. The…
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…
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 256 - evalbatchsize: 256 - lrschedulertype: linear - lrschedulerwarmupsteps: 4000 - numepochs: 20 - Transformers 4.30.2 - Pytorch 2.11.0+cu130 - Datasets 4.1.1 - Tokenizers 0.13.3
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
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.
Collection · 4 entries
Models that fit 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.
Collection · 6 entries
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.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
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
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.