"Less Gemma - more human" I had a lot of fresh and creative experience with both ConicCat tunes, but they had some instability issues, that I hopefully fixed there.
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.
12,579Models
3,583Datasets
291Papers
7,578Publishers
12,850Sourced relationships
Updated 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
Quantized version of Ateron/Gemma-4-Writers-31B-V2. Offline-quantized W8A16 FP8 in the float-quantized format: weights in float8e4m3fn with per-output-channel symmetric scales, activations kept in bf16/fp16. For each linear layer, every output row gets its own scale starting from amax / 448, refined by an MSE clip search over ~9 clip fractions (0.8–1.0× amax) picking the lowest-error scale per row. Weights are then quantized q = e4m3(w / scale) with round-to-nearest and saturation. This per-channel + clipping scheme gives better SNR than vLLM's online per-tensor --quantization fp8 path. Only 2D linear projection weights are quantized (attention q/k/v/o, MLP gate/up/down). Embeddings, norms…
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…
Quantized version of Ateron/Gemma-4-Writers-31B. Offline-quantized W8A16 FP8 in the float-quantized format: weights in float8e4m3fn with per-output-channel symmetric scales, activations kept in bf16/fp16. For each linear layer, every output row gets its own scale starting from amax / 448, refined by an MSE clip search over ~9 clip fractions (0.8–1.0× amax) picking the lowest-error scale per row. Weights are then quantized q = e4m3(w / scale) with round-to-nearest and saturation. This per-channel + clipping scheme gives better SNR than vLLM's online per-tensor --quantization fp8 path. Only 2D linear projection weights are quantized (attention q/k/v/o, MLP gate/up/down). Embeddings, norms…
weighted/imatrix quants of https://huggingface.co/Ateron/Gemma-4-Writers-31B 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-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…
weighted/imatrix quants of https://huggingface.co/ConicCat/Gemma4-Writer-26BA4B 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-26BA4B-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…
weighted/imatrix quants of https://huggingface.co/ConicCat/Gemma4-Writer-31B-F 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-F-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…
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…
BeaverAI proudly presents... - Thank you to each and everyone who donated and subscribed in Ko-Fi to make our venture a little bit easier. - I'm also recently unemployed. I am a Software Developer with 8 years of experience in Web, API, AI, and adapting to new tech and requirements. If you're hiring, feel free to reach out to me however. An upscaled Gemma 2B tune with modern techniques. Au Revoir, Gemma!
This private repository stores checkpoints from the GeoEvidence 4B answer-RL experiments. The vision-only and full-parameter runs start from the same Qwen3-VL-4B-Thinking base model and use separate run names. Checkpoints are uploaded under runs/ /globalstep /. The loadable model for a checkpoint is in its actor/huggingface/ subfolder. The run name, step, and commit ID are also recorded in the training output's GR1HFSYNC.json marker after upload verification. Evaluation uses the frozen Final483 dataset. See the project experiment record for the exact prompt, decoding protocol, dataset identity, and results.
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
OLMo-style model trained on the deduplicated Pile, one of the 108 models released with (ICML 2026). Disentangling Geometry, Performance, and Training in Language Models Atharva Kulkarni, Jacob Mitchell Springer, Arjun Subramonian, Swabha Swayamdipta ICML 2026 (PMLR 306) · Paper · The paper asks whether the geometry of a language model's unembedding matrix, in particular its effective rank, can estimate downstream performance. Using a suite of 108 OLMo-style models (4M-75M parameters) trained under controlled variations of batch size, weight decay, learning rate, token budget and final LR, it finds that the relationship is inconsistent: high effective rank often accompanies the best models…
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.