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).
Open-weight model · Question answering
nlp-lab5-8-qa
by Boorapha Mahattheeranangkun Boorapha/nlp-lab5-8-qa
nlp-lab5-8-qa is an open-weight model for question answering from Boorapha Mahattheeranangkun. It has 105M parameters and a 512-token context. 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.
Thai extractive QA fine-tuned จาก airesearch/wangchanberta-base-att-spm-uncased ด้วย 4 ตัวอย่าง (engine: transformers, steps: 30) ข้อสอบปลายภาควิชา 10301374 โดย 6704101346 ข้อมูลฝึกมีเพียง 4 คู่คำถาม-คำตอบ โมเดลจึงจดจำรูปแบบเฉพาะ (overfit)…
Parameters105M
Context512
Weights418.6 MB
License—
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve nlp-lab5-8-qa (105M 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-qa on every accelerator the SAVRN Index prices, at every precision
Model Card
Thai extractive QA fine-tuned จาก airesearch/wangchanberta-base-att-spm-uncased ด้วย 4 ตัวอย่าง (engine: transformers, steps: 30) ข้อสอบปลายภาควิชา 10301374 โดย 6704101346 ข้อมูลฝึกมีเพียง 4 คู่คำถาม-คำตอบ โมเดลจึงจดจำรูปแบบเฉพาะ (overfit) มากกว่าเรียนรู้การอ่านเข้าใจ ผลบนข้อมูลที่ฝึกจึงไม่สะท้อนความสามารถจริง ไม่มีชุด validation ที่เป็นอิสระ ค่าที่วัดได้แปรปรวนสูงตาม seed และคำถามส่วนใหญ่เป็นแบบ 'อยู่ภาคใด' ทำให้โมเดลอาจเดาจากรูปแบบ ควรใช้ชุดข้อมูลขนาดใหญ่ เช่น thaiqasquad หรือ iappwikiqasquad และแยก train/validation/test ก่อนสรุปผล
Excerpt from the card by Boorapha Mahattheeranangkun.
Configuration
- Architecture
- CamembertForQuestionAnswering
- Context length (tokens)
- 512
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 25,005
- Model type
- camembert
Identity and Version
- Repository
- Boorapha/nlp-lab5-8-qa
- Publisher
- Boorapha Mahattheeranangkun
- Task
- Question answering
- Modality
- Text
- Library
- Not stated by the source
- Parameters
- 105M parameters
- Languages
- th
- Revision
- 4e0ca63a132f209c8c5a6a474c9b45de7757968e
- First published
- 2026-10-09
- Last updated
- 2026-10-09
Files and Weights
6 files, 420.8 MB in total. The weights are 1 file totalling 418.6 MB in safetensors.
Weights1 file · 418.6 MB
Configuration1 file · 752 B
Tokenizer2 files · 2.2 MB
Documentation1 file · 1.4 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 418.6 MB | 8da4b22b127b |
| config.json | Configuration | 752 B | — |
| README.md | Documentation | 1.4 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 2.2 MB | — |
| tokenizer_config.json | Tokenizer | 465 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 418.6 MB
Download from Boorapha Mahattheeranangkun
Released by Boorapha Mahattheeranangkun through its official repository on Hugging Face.
Built From
- Derived from airesearch/wangchanberta-base-att-spm-uncased
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 418.6 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 nlp-lab5-8-qa
How much GPU memory does nlp-lab5-8-qa need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (105M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run nlp-lab5-8-qa 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.
What is nlp-lab5-8-qa's context length?
512 tokens, from the maximum position embeddings in its published configuration.
Similar Models
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).
Thai extractive QA fine-tuned from airesearch/wangchanberta-base-att-spm-uncased with SimpleTransformers (10301374 NLP final exam, Q07).
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).