nectec/pathumma-thaillm-8b-think-3.0.0-GGUF

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Pathumma-ThaiLLM-Think-3.0.0

Post-trained Thai Large Language Model built upon the foundation model from the Thai national initiative **ThaiLLM**.

This release applies multi-stage Supervised Fine-Tuning (SFT) to enhance:

• —Instruction following
• —Structured tool / function calling
• —Mathematical and coding competence
• —Multi-step analytical capability
• —Thai–English bilingual robustness

Training Strategy

Post-training is organized into two stages:

• —Stage 1: Instruction & Tool-Calling Alignment
• —Stage 2: Reasoning Specialization

For selected corpora, only curated subsets were used to maintain domain balance.

Stage 1: Instruction & Tool-Calling Alignment

Focus areas:

• —Instruction compliance
• —Structured tool-call formatting
• —General Thai task robustness
• —STEM-oriented instruction alignment

Datasets

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Stage 2: Reasoning Specialization

Focus areas:

• —Multi-step mathematical analysis
• —Code understanding and synthesis
• —Structured analytical responses
• —Tool-calling with explicit reasoning traces
• —Thai reasoning distillation

Datasets

Note: For selected datasets, curated subsets were employed to ensure balanced domain representation.

Methodology

• —Base model: ThaiLLM foundation model
• —Training objective: Supervised Fine-Tuning (SFT)
• —Two-stage curriculum design
• —Domain-balanced optimization
• —Tool-call schema alignment
• —Thai reasoning distillation

Compute Infrastructure

Training was conducted on the LANTA high-performance computing cluster, utilizing 16 nodes (64×A100 40GB GPUs) for distributed large-scale post-training.

Capabilities

• —Thai instruction compliance
• —Structured JSON tool invocation
• —Mathematical problem solving
• —Code generation and analysis
• —Multi-step analytical tasks
• —Thai–English bilingual support

Limitations

• —May hallucinate if tool schema is incomplete
• —Performance on long analytical chains may degrade without retrieval
• —Domain coverage depends on included corpora

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Quickstart

The code of Qwen3 has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers. With transformers<4.51.0, you will encounter the following error:

KeyError: 'qwen3'

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "nectec/pathumma-thaillm-8b-think-3.0.0"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
# prepare the model input
prompt = "ทำไมวงกลมถึงมี 360 องศา"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content) # no opening <think> tag
print("content:", content)

For deployment, you can use vllm>=0.8.5 to create an OpenAI-compatible API endpoint:

vllm serve nectec/pathumma-thaillm-8b-think-3.0.0 \
  --enforce-eager \
  --no-enable-chunked-prefill \
  --tool-call-parser hermes

For local use, applications such as Ollama, LMStudio, and llama.cpp have also supported.

About the Project

Pathumma-ThaiLLM-Think-3.0.0 is part of ongoing research toward sovereign Thai large language models optimized for analytical and tool-augmented intelligence.

Contributor Contract

LLM Team <br> Piyawat Chuangkrud (piyawat@it.kmitl.ac.th)<br> Chanon Utupon (s6401001620165@email.kmutnb.ac.th)<br> Jessada Pranee (jessada.pran@kmutt.ac.th)<br> Arnon Saeoung (anon.saeoueng@gmail.com)<br> Chaianun Damrongrat (chaianun.damrongrat@nectec.or.th)<br> Sarawoot Kongyoung (sarawoot.kongyoung@nectec.or.th)

Dataset Contributors

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