OpenThaiGPT 7B 1.0.0 Chat
A 7B Thai-language chat model built on LLaMA 2, pretrained on 65B+ Thai words and instruction-tuned on 1M+ Thai examples. Adds 10,000 common Thai vocabulary tokens to the base model, which the vendor reports as a 10× throughput improvement for Thai text. Scored 38.40% average on a set of Thai academic exams — highest among open-source Thai models at release.
If you need a commercially usable Thai chat model at 7B, this was the credible open-source baseline at its release. The vocab-extension speed claim is real and matters for Thai throughput. That said, 38.40% on exams means it will hallucinate and misfire on anything requiring reliable knowledge — treat it as a fluent Thai speaker, not a Thai expert. Worth running if Thai is your primary language and you're on a VRAM budget; hedge on any fact-sensitive workload.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.00/10. License (llama2) matches the HF card and commercial-OK is correct under Llama 2 terms. Params, context (4096), family, and vendor all verify against the card. The description and verdict are operator-voiced: they cite the 38.40% exam number honestly, frame it as a low absolute floor, and warn against fact-sensitive use. bestUseCase is appropriately narrow (Thai instruction-following), and weaknesses cover context length, non-Thai degradation, and base-model inheritance. Brand fit is solid: a commercially usable Thai 7B with GGUF available is exactly the kind of niche local-AI option runlocalai readers benefit from knowing about.
Overview
A 7B Thai-language chat model built on LLaMA 2, pretrained on 65B+ Thai words and instruction-tuned on 1M+ Thai examples. Adds 10,000 common Thai vocabulary tokens to the base model, which the vendor reports as a 10× throughput improvement for Thai text. Scored 38.40% average on a set of Thai academic exams — highest among open-source Thai models at release.
Strengths
- Pretrained on 65B+ Thai words with 1M+ Thai instruction-tuning examples
- 10,000 added Thai vocab tokens reduce tokenization overhead and speed up generation
- 38.40% average on Thai exam benchmarks — top-reported score among open-source Thai LLMs at time of release
- Commercial use permitted under LLaMA 2 license terms
Weaknesses
- 38.40% exam average is the best open-source Thai score at release — but still a low absolute number; do not rely on it for factual-critical tasks
- 4096-token context window is tight for long documents or multi-turn sessions
- Non-Thai language quality degrades significantly due to Thai-focused training
- Inherits LLaMA 2 base model biases and safety limitations
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 3.9 GB | 5 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of OpenThaiGPT 7B 1.0.0 Chat.
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Frequently asked
What's the minimum VRAM to run OpenThaiGPT 7B 1.0.0 Chat?
Can I use OpenThaiGPT 7B 1.0.0 Chat commercially?
What's the context length of OpenThaiGPT 7B 1.0.0 Chat?
Source: huggingface.co/openthaigpt/openthaigpt-1.0.0-7b-chat
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
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