For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689 CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the vinai's bertweet-base language model on the cryptocurrency domain, using a corpus of over 3.2M unique cryptocurrency-related social media posts. (A research paper with more details will follow soon.) The model was trained on the following labels: "Bearish": 0, "Neutral": 1, "Bullish": 2 CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, sampled from…
Open-weight model · Text classification
sentiment-analysis-deploy
by GM gm84/sentiment-analysis-deploy
sentiment-analysis-deploy is a model for text classification from GM (access requested at publisher). It has 125M parameters. At 16-bit it needs about 0.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 38 downloads a month.
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.
Parameters125M
Context—
Weights498.6 MB
License—
AccessAccess requested at publisher
Monthly Downloads38
Runs On
What it takes to serve sentiment-analysis-deploy (125M 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 | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x 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, read Oct 22, 2026.
sentiment-analysis-deploy on every accelerator the SAVRN Index prices, at every precision
Model Card
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).
Excerpt from the card by GM.
Identity and Version
- Repository
- gm84/sentiment-analysis-deploy
- Publisher
- GM
- Task
- Text classification
- Modality
- Text
- Library
- Not stated by the source
- Parameters
- 125M parameters
- Languages
- Not stated by the source
- Revision
- 3671505ca9f51b30554f57a21c74069fc5edd618
- First published
- 2026-09-07
- Last updated
- 2026-10-19
Files and Weights
6 files, 502.2 MB in total. The weights are 1 file totalling 498.6 MB in safetensors.
Weights1 file · 498.6 MB
Configuration1 file · 974 B
Tokenizer2 files · 3.6 MB
Documentation1 file · 5.1 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 498.6 MB | — |
| config.json | Configuration | 974 B | — |
| README.md | Documentation | 5.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer_config.json | Tokenizer | 416 B | — |
License and Download
- License
- Not stated by the source
- Access
- Access requested at publisher
- Download size
- 498.6 MB
Request access from GM
GM grants access through its official repository on Hugging Face.
Built From
- Described by arXiv:1910.09700
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 498.6 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About sentiment-analysis-deploy
How much GPU memory does sentiment-analysis-deploy need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (125M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run sentiment-analysis-deploy 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.
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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).
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