nlp-lab5-8-qa is an open-weight model from Akarawut Chodchoi. 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 airesearch/wangchanberta-base-att-spm-uncased with SimpleTransformers (10301374 NLP final exam Q07).
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 19, 2026.
nlp-lab5-8-qa on every accelerator the SAVRN Index prices, at every precision
Model Card
Thai extractive QA: fine-tuned airesearch/wangchanberta-base-att-spm-uncased with SimpleTransformers (10301374 NLP final exam Q07). - trainingexamples: 4 - ฝึกด้วยข้อมูลเพียง 4 คู่คำถาม-คำตอบ โมเดลจึงจำข้อมูลฝึก (overfit) มากกว่าเรียนรู้การตอบคำถามทั่วไป - ชุดทดสอบใช้ context เดียวกับตอนฝึก ผลจึงประเมินความสามารถจริงสูงเกินไป - หากต้องการใช้งานจริงควรฝึกกับชุดข้อมูลขนาดใหญ่ เช่น iappwikiqasquad ก่อน
Excerpt from the card by Akarawut Chodchoi.
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
- Akarawut/nlp-lab5-8-qa
- Publisher
- Akarawut Chodchoi
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 105M parameters
- Languages
- Not stated by the source
- Revision
- 95b4c41078593c3d3db1050d3dc3bcddcde0e0dd
- 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.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 418.6 MB | 3512469373bd |
| config.json | Configuration | 752 B | — |
| README.md | Documentation | 1.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 2.2 MB | — |
| tokenizer_config.json | Tokenizer | 491 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 418.6 MB
Released by Akarawut Chodchoi through its official repository on Hugging Face.
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.