OpenThai SystemOne
Collection
The Fast SystemOne Model for General Classification / Computer Use / Automation • 9 items • Updated
OpenThai-SystemOne — W8A8-INT8
OpenThai-SystemOne is an open Thai + English System One decision model: one forward
pass answers typed questions (choice over up to 255 options, ordinal score, yes/no noul) about a text / JSON state with
calibrated probabilities, no text generation. It is a Qwen3.5-0.8B text tower (Thai continued pre-training) plus a 256-slot
decision head. This repo is a quantization of v0.3 (commit f3709948).
What is quantized: the Linear layers of the tower. The token embeddings, the 256-slot decision head and the per-type temperatures stay in bf16. Quantization therefore only perturbs the hidden state the head reads.
Format: INT8 W8A8 (dynamic per-token activations). compressed-tensors (llm-compressor) checkpoint. Loaded through transformers the weights are decompressed to bf16 at load time (same speed as bf16, smaller download); native FP8 / INT8 / FP4 kernels need a runtime with this architecture (the decision head is custom, so vLLM does not serve it out of the box).
Size: 1009 MB (bf16 original: 1,509 MB).
Usage
pip install torch transformers safetensors pydantic && pip install compressed-tensors
from transformers import AutoModel, AutoTokenizer # trust_remote_code files are in this repo
model = AutoModel.from_pretrained("iapp/OpenThai-SystemOne-W8A8-INT8", trust_remote_code=True)
# or with the pip package (git+https://github.com/iapp-technology/openthai-systemone):
from openthai_systemone import SystemOneClient
c = SystemOneClient("iapp/OpenThai-SystemOne-W8A8-INT8")
r = c.system_one("ร้านนี้อาหารอร่อยมาก แต่รอนานเกือบชั่วโมง", {"sentiment": {"type": "choice", "instructions": "ความรู้สึก",
"criteria": {"บวก": None, "ลบ": None, "กลาง": None}}})
Accuracy vs the bf16 original (same records, single option order, first 800 per set)
Macro: public 73.7 (original 74.3), Thai 79.9 (original 80.1).
| subset | bf16 original | this | Δ |
|---|---|---|---|
| public 13-subset bench | |||
| aegis2 (noul) | 83.2 | 82.8 | -0.4 |
| boolq (noul) | 79.7 | 79.7 | +0.0 |
| civil_comments (noul) | 79.0 | 78.7 | -0.3 |
| helpsteer2 (score) | 41.6 | 41.6 | +0.0 |
| massive-de-DE (choice) | 88.3 | 88.3 | +0.0 |
| massive-en-US (choice) | 88.3 | 88.3 | +0.0 |
| multinli (choice) | 89.0 | 89.3 | +0.3 |
| paws (noul) | 94.0 | 93.2 | -0.8 |
| pubmedqa (choice) | 64.0 | 63.2 | -0.8 |
| squad2 (noul) | 89.3 | 88.0 | -1.3 |
| summeval-consistency (score) | 75.0 | 75.0 | +0.0 |
| summeval-relevance (score) | 21.7 | 18.3 | -3.3 |
| vitaminc-dev (choice) | 72.5 | 72.0 | -0.5 |
| macro, public 13-subset bench | 74.3 | 73.7 | -0.6 |
| Thai held-out / eval sets | |||
| banking77 (choice) | 59.1 | 58.8 | -0.4 |
| contrastive_th (choice) | 80.7 | 81.1 | +0.3 |
| contrastive_th (noul) | 83.5 | 84.3 | +0.8 |
| contrastive_th (score) | 78.6 | 75.0 | -3.6 |
| massive_th (choice) | 90.6 | 90.1 | -0.5 |
| prachathai (choice) | 98.3 | 98.3 | +0.0 |
| prachathai (noul) | 93.4 | 93.1 | -0.3 |
| sib200_th (choice) | 77.9 | 78.9 | +1.0 |
| wisesight (choice) | 48.9 | 49.8 | +0.9 |
| wongnai (score) | 64.5 | 65.1 | +0.6 |
| xlam_tools (choice) | 99.4 | 99.4 | +0.0 |
| xnli_th (choice) | 79.8 | 79.1 | -0.6 |
| xnli_th (noul) | 86.8 | 86.0 | -0.8 |
| macro, Thai held-out / eval sets | 80.1 | 79.9 | -0.2 |
Notes
- Scores are single-option-order accuracy on the first 800 records of each set (
scripts/06_eval.py --limit 800), the same records for the original and the quantization.scoresubsets report exact level accuracy. - Base model, data, training and the full benchmark tables: iapp/OpenThai-SystemOne.
- License Apache-2.0 (same as the base). Built by iApp Technology / OpenThaiGPT.
- Downloads last month
- 1