โมเดล Thai GPT-2 ที่ได้รับการ Fine-tune โดยนักศึกษา (revespera) สำหรับงานถาม-ตอบภาษาไทยจากชุดข้อมูลโรค 3,000 คู่ 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).
Open-weight model · Text generation
6704101366
by Wachirawit Panyasriwichai Wachirawit2005/6704101366
6704101366 is an open-weight model for text generation from Wachirawit Panyasriwichai. It has 124M 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.
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.
Parameters124M
Context—
Weights497.8 MB
License—
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve 6704101366 (124M 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 18, 2026.
6704101366 on every accelerator the SAVRN Index prices, at every precision
Model Card
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).
Excerpt from the card by Wachirawit Panyasriwichai.
Configuration
- Architecture
- GPT2LMHeadModel
- Vocabulary size
- 50,269
- Model type
- gpt2
Identity and Version
- Repository
- Wachirawit2005/6704101366
- Publisher
- Wachirawit Panyasriwichai
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 124M parameters
- Languages
- Not stated by the source
- Revision
- 262a5970445d30a252a6aef636c601c4faa59330
- First published
- 2026-10-09
- Last updated
- 2026-10-09
Files and Weights
7 files, 502.3 MB in total. The weights are 1 file totalling 497.8 MB in safetensors.
Weights1 file · 497.8 MB
Configuration2 files · 1.1 KB
Tokenizer2 files · 4.5 MB
Documentation1 file · 5.2 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 497.8 MB | 9fc4c2837442 |
| config.json | Configuration | 998 B | — |
| generation_config.json | Configuration | 140 B | — |
| README.md | Documentation | 5.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 4.5 MB | — |
| tokenizer_config.json | Tokenizer | 343 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 497.8 MB
Download from Wachirawit Panyasriwichai
Released by Wachirawit Panyasriwichai through its official repository on Hugging Face.
Built From
- Described by arXiv:1910.09700
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 497.8 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 6704101366
How much GPU memory does 6704101366 need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (124M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run 6704101366 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.
Similar Models
โมเดล Thai GPT-2 ที่ Fine-tune จาก flax-community/gpt2-base-thai ด้วย THAITEXTS ข้อสอบปลายภาค 10301374 โดย 6704101346 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 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 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).