# ESMC-6B by Biohub: Open-Weight Model Source: https://savrn.com/models/esmc-6b Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve ESMC-6B (6.4B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 12.7 GB | 15.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 6.4 GB | 7.6 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 3.2 GB | 3.8 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 24, 2026. [ESMC-6B on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/esmc-6b/gpus) ## SAVRN's Notes on ESMC-6B ESMC 6B is a protein language model: it learned the patterns of protein biology from billions of sequences and turns a protein into a representation that downstream models can use for function, structure or design work. Biohub publishes it, and 2.9 million downloads a month show it is already a standard tool. At 6.4B parameters it fits on one modern GPU at 16-bit, about 15 GB with working room. The MIT license allows commercial use, which is notable in a field where many strong models carry noncommercial terms. For therapeutic or industrial protein work, this is a foundation worth testing early. SAVRN Research, 2026-09-18 ## Model Card By Biohub, published under mit, revision af1602ba7406. ### ESMC ### Model Details ESMC is a state-of-the-art protein language model that has learned the rules of protein biology from training on billions of protein sequences. ESMC provides representations of proteins enabling novel AI applications from therapeutic protein engineering to unlocking basic insights into protein biology across life. The ESMC 6B model has 6 billion parameters, with 80 layers and 2.37e23 training flops. We additionally release overtrained 300M and 600M parameter variants of ESMC for local inference and finetuning. The [ESMFold2](https://huggingface.co/biohub/ESMFold2) structure prediction models are trained on top of a frozen ESMC 6B language model. ESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy. The [ESMC sparse autoencoder](https://huggingface.co/biohub/ESMC-6B-sae-layer60-k64-codebook16384), ESMC-6B-sae-layer60-k64-codebook16384, is built on the ESMC 6B model and provides human-interpretable, agent-generated feature descriptions. See the [ESMC SAE overview card](https://huggingface.co/biohub/ESMC-SAE-Overview) for the full set of ESMC SAE variants. [Read the full model card (1,376 words)](https://savrn.com/models/esmc-6b/card) ## Configuration Architecture EsmcForMaskedLM Context length (tokens) 2,048 Layers 80 Hidden size 2,560 Feed-forward size 6,912 Attention heads 40 Key/value heads 40 Head dimension 64 Vocabulary size 64 Model type esmc ## Identity and Version Repository biohub/ESMC-6B Publisher Biohub Task Fill mask Modality Text Library transformers Parameters 6.4B parameters Languages en Revision af1602ba7406f521b11bf8f81d52af378cde09e4 First published 2026-05-19 Last updated 2026-09-16 ## Files and Weights 15 files, 25.4 GB in total. The weights are 6 files totalling 25.4 GB in safetensors. Weights6 files · 25.4 GB Configuration4 files · 92.1 KB Tokenizer2 files · 3.2 KB Documentation1 file · 15.0 KB Other1 file · 30.2 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model-00001-of-00006.safetensors | Weights | 4.9 GB | 80cc4a51f218 | | model-00002-of-00006.safetensors | Weights | 5.0 GB | 273740475dcd | | model-00003-of-00006.safetensors | Weights | 5.0 GB | 00769f0e680a | | model-00004-of-00006.safetensors | Weights | 4.9 GB | 1287dd34cb55 | | model-00005-of-00006.safetensors | Weights | 5.0 GB | fef715f2b9f0 | | model-00006-of-00006.safetensors | Weights | 582.8 MB | 7f88f2e9a884 | | config.json | Configuration | 1.1 KB | — | | model.safetensors.index.json | Configuration | 85.7 KB | — | | modeling_esmc_remote.py | Configuration | 5.1 KB | — | | special_tokens_map.json | Configuration | 171 B | — | | README.md | Documentation | 15.0 KB | — | | images/contact_pal.png | Other | 30.2 KB | — | | .gitattributes | Repository | 1.6 KB | — | | tokenizer.json | Tokenizer | 2.9 KB | — | | tokenizer_config.json | Tokenizer | 327 B | — | ## License and Download License mit Access Open weights, no gate Download size 25.4 GB [Download from Biohub](https://huggingface.co/biohub/ESMC-6B) Released by Biohub through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 25.4 GB | | 16-bit | 12.7 GB | | 8-bit | 6.4 GB | | 4-bit | 3.2 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About ESMC-6B ### How much GPU memory does ESMC-6B need? About 15.2 GB at 16-bit and 3.8 GB at 4-bit: the weights (6.4B parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run ESMC-6B on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use ESMC-6B commercially? Yes. ESMC-6B is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ### What is ESMC-6B's context length? 2,048 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Fill mask ### [minerva-mlm](https://savrn.com/models/minerva-mlm) [Garyk Brixi](https://savrn.com/model-publishers/gbrixi) Minerva-MLM is a genome language model for coevolutionary mining. The checkpoint includes the masked language model head and three interaction heads for base-pairing, repeat, and structure signals. Full documentation, finetuning scripts, notebooks, and setup are on the github. Try it in the browser here. Inference from this repo needs only Transformers and PyTorch: flash-attn is picked up automatically when present, otherwise Minerva falls back to PyTorch SDPA. The optional minerva-dna package adds plotting helpers, GenBank utilities and finetuning wrappers, and lets you load the model without trustremotecode: With minerva-dna installed, import the class directly and drop trustremotecode… Open weights apache-2.0 671M parameters transformers [View model](https://savrn.com/models/minerva-mlm) Model · Fill mask ### [minerva-mlm-8k](https://savrn.com/models/minerva-mlm-8k) [Garyk Brixi](https://savrn.com/model-publishers/gbrixi) Minerva-MLM is a genome language model for coevolutionary mining. The checkpoint includes the masked language model head and three interaction heads for base-pairing, repeat, and structure signals. Full documentation, finetuning scripts, notebooks, and setup are on the github. Try it in the browser here. Inference from this repo needs only Transformers and PyTorch: flash-attn is picked up automatically when present, otherwise Minerva falls back to PyTorch SDPA. The optional minerva-dna package adds plotting helpers, GenBank utilities and finetuning wrappers, and lets you load the model without trustremotecode: With minerva-dna installed, import the class directly and drop trustremotecode… Open weights apache-2.0 671M parameters transformers [View model](https://savrn.com/models/minerva-mlm-8k) Model · Fill mask ### [esm2_t33_650M_UR50D](https://savrn.com/models/esm2-t33-650m-ur50d) [AI at Meta](https://savrn.com/model-publishers/facebook) ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the accompanying paper. You may also be interested in some demo notebooks (PyTorch, TensorFlow) which demonstrate how to fine-tune ESM-2 models on your tasks of interest. Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train Open weights mit 652M parameters 1,026 tokens transformers [View model](https://savrn.com/models/esm2-t33-650m-ur50d) Model · Fill mask ### [xlm-roberta-large](https://savrn.com/models/xlm-roberta-large) [Facebook AI community](https://savrn.com/model-publishers/facebookai) XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository. Disclaimer: The team releasing XLM-RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team. XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. RoBERTa is a transformers model pretrained on a large corpus in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in… Open weights mit 561M parameters 514 tokens transformers [View model](https://savrn.com/models/xlm-roberta-large) Model · Fill mask ### [ModernBERT-large](https://savrn.com/models/modernbert-large) [Answer.AI](https://savrn.com/model-publishers/answerdotai) ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-style) pre-trained on 2 trillion tokens of English and code data with a native context length of up to 8,192 tokens. ModernBERT leverages recent architectural improvements such as: - Rotary Positional Embeddings (RoPE) for long-context support. - Local-Global Alternating Attention for efficiency on long inputs. - Unpadding and Flash Attention for efficient inference. ModernBERT’s native long context length makes it ideal for tasks that require processing long documents, such as retrieval, classification, and semantic search within large corpora. The model was trained on a large corpus of text and code, making it… Open weights apache-2.0 396M parameters 8,192 tokens transformers [View model](https://savrn.com/models/modernbert-large) Model · Fill mask ### [ModernBERT-Large-Instruct-Logician-v0.5](https://savrn.com/models/modernbert-large-instruct-logician-v0-5) [John Gorriceta](https://savrn.com/model-publishers/johngorri) This model is a fine-tuned version of ModernBERT optimized for logical reasoning, deductive analysis, and structure-based token prediction. It relies on the ModernBertForMaskedLM architecture, making it highly effective at filling in missing contextual logic tokens (fill-mask). - As an encoder-based Masked Language Model, it is not designed for long-form generative text (like ChatGPT). It excels at predicting masked tokens inside structured prompts. Open weights apache-2.0 396M parameters 8,192 tokens [View model](https://savrn.com/models/modernbert-large-instruct-logician-v0-5) ## Biohub [All models and datasets](https://savrn.com/model-publishers/biohub) ## Versions - [af1602ba7406](https://savrn.com/models/esmc-6b/versions/af1602ba7406) · current 2026-10-05 ## Explore More - [All fill mask models](https://savrn.com/models/tasks/fill-mask) - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/biohub/ESMC-6B) - [How the hub is built](https://savrn.com/model-hub/methodology)