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
nlp-lab5-8-gpt2
by Boorapha Mahattheeranangkun Boorapha/nlp-lab5-8-gpt2
nlp-lab5-8-gpt2 is an open-weight model for text generation from Boorapha Mahattheeranangkun, released under MIT License. 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.
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.
Parameters124M
Context—
Weights497.8 MB
Licensemit
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve nlp-lab5-8-gpt2 (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.
nlp-lab5-8-gpt2 on every accelerator the SAVRN Index prices, at every precision
Model Card
By Boorapha Mahattheeranangkun, published under mit, revision b61557405128.
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).
Read Boorapha Mahattheeranangkun's full model card
Model Card for Model ID
Model Details
Model Description
โมเดล Thai GPT-2 ที่ Fine-tune จาก flax-community/gpt2-base-thai ด้วย THAI_TEXTS ข้อสอบปลายภาค 10301374 โดย 6704101346
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- Language(s) (NLP): th
- License: mit
- Finetuned from model [optional]: flax-community/gpt2-base-thai
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Bias, Risks, and Limitations
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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
- GPT2LMHeadModel
- Vocabulary size
- 50,269
- Model type
- gpt2
Identity and Version
- Repository
- Boorapha/nlp-lab5-8-gpt2
- Publisher
- Boorapha Mahattheeranangkun
- Task
- Text generation
- Modality
- Text
- Library
- Not stated by the source
- Parameters
- 124M parameters
- Languages
- th
- Revision
- b61557405128b7823c8db2061d659eab654a05fe
- 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 · 5.3 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 497.8 MB | 46ececcf2d6d |
| config.json | Configuration | 998 B | — |
| generation_config.json | Configuration | 140 B | — |
| README.md | Documentation | 5.3 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 4.5 MB | — |
| tokenizer_config.json | Tokenizer | 343 B | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 497.8 MB
Download from Boorapha Mahattheeranangkun
Released by Boorapha Mahattheeranangkun through its official repository on Hugging Face. Read the license.
Built From
- Derived from flax-community/gpt2-base-thai
- 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 nlp-lab5-8-gpt2
How much GPU memory does nlp-lab5-8-gpt2 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 nlp-lab5-8-gpt2 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 nlp-lab5-8-gpt2 commercially?
Yes. nlp-lab5-8-gpt2 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.
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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).
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).