What it is. An extractive question-answering model. Given a question and a short typed answer, it returns the phrase in the answer that answers the question, or nothing. It cannot generate text. It is the reading part of rai, a tool that says only whether a description is complete under a notion of completeness written down in advance. Every decision after the reading is made in plain code. Trained from. deepset/minilm-uncased-squad2 (CC-BY-4.0), deepset's fine-tune of Microsoft's MiniLM-L12-H384-uncased (MIT) on SQuAD 2.0. Same architecture, 33M parameters, nothing added. Credit to deepset and Microsoft. 0.3.7 trains on 0.3.6's rows and 5,800 more written in Claude Code. In 0.3.6's rows…
Open-weight model · Question answering
basic-chat-model
by Lenin Villanueva leninangelov/basic-chat-model
basic-chat-model is an open-weight model for question answering from Lenin Villanueva, released under Apache License 2.0. It has 31M parameters. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 14 downloads a month.
This modelcard aims to be a base template for new models. It has been generated using this raw template. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Parameters31M
Context—
Weights122.2 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads14
Runs On
What it takes to serve basic-chat-model (31M 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 | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 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 9, 2026.
basic-chat-model on every accelerator the SAVRN Index prices, at every precision
Model Card
By Lenin Villanueva, published under apache-2.0, revision e0ae8f7c68dd.
This modelcard aims to be a base template for new models. It has been generated using this raw template. 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).
Read Lenin Villanueva's full model card
Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Summary
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Configuration
- Architecture
- T5ForConditionalGeneration
- Vocabulary size
- 14,000
- Stored precision
- float32
- Model type
- t5
Identity and Version
- Repository
- leninangelov/basic-chat-model
- Publisher
- Lenin Villanueva
- Task
- Question answering
- Modality
- Text
- Library
- Not stated by the source
- Parameters
- 31M parameters
- Languages
- es
- Revision
- e0ae8f7c68dd1b6f0a9ad72a1ca871fe9b6c946c
- First published
- 2024-10-30
- Last updated
- 2026-09-21
Files and Weights
8 files, 122.3 MB in total. The weights are 1 file totalling 122.2 MB in safetensors.
Weights1 file · 122.2 MB
Configuration3 files · 3.5 KB
Tokenizer2 files · 91.0 KB
Documentation1 file · 5.4 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 122.2 MB | ed38940b2924 |
| config.json | Configuration | 717 B | — |
| generation_config.json | Configuration | 153 B | — |
| special_tokens_map.json | Configuration | 2.7 KB | — |
| README.md | Documentation | 5.4 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 69.4 KB | — |
| tokenizer_config.json | Tokenizer | 21.6 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 122.2 MB
Download from Lenin Villanueva
Released by Lenin Villanueva through its official repository on Hugging Face. Read the license.
Built From
- Derived from google-t5/t5-small
- Described by arXiv:1910.09700
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 122.2 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About basic-chat-model
How much GPU memory does basic-chat-model need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (31M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run basic-chat-model 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 basic-chat-model commercially?
Yes. basic-chat-model is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
Similar Models
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