Instructions to use nectec/Pathumma-llm-text-1.0.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nectec/Pathumma-llm-text-1.0.0 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nectec/Pathumma-llm-text-1.0.0",
	filename="Pathumma-llm-it-7b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use nectec/Pathumma-llm-text-1.0.0 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M

Use Docker

docker model run hf.co/nectec/Pathumma-llm-text-1.0.0:Q4_K_M

LM Studio
Jan

vLLM

How to use nectec/Pathumma-llm-text-1.0.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nectec/Pathumma-llm-text-1.0.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nectec/Pathumma-llm-text-1.0.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nectec/Pathumma-llm-text-1.0.0:Q4_K_M

Ollama
How to use nectec/Pathumma-llm-text-1.0.0 with Ollama:
```
ollama run hf.co/nectec/Pathumma-llm-text-1.0.0:Q4_K_M
```

Unsloth Studio new

How to use nectec/Pathumma-llm-text-1.0.0 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nectec/Pathumma-llm-text-1.0.0 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nectec/Pathumma-llm-text-1.0.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nectec/Pathumma-llm-text-1.0.0 to start chatting

Pi new

How to use nectec/Pathumma-llm-text-1.0.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nectec/Pathumma-llm-text-1.0.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nectec/Pathumma-llm-text-1.0.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default nectec/Pathumma-llm-text-1.0.0:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use nectec/Pathumma-llm-text-1.0.0 with Docker Model Runner:
```
docker model run hf.co/nectec/Pathumma-llm-text-1.0.0:Q4_K_M
```

Lemonade

How to use nectec/Pathumma-llm-text-1.0.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nectec/Pathumma-llm-text-1.0.0:Q4_K_M

Run and chat with the model

lemonade run user.Pathumma-llm-text-1.0.0-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

PathummaLLM-text-1.0.0-7B: Thai & China & English Large Language Model Instruct

PathummaLLM-text-1.0.0-7B is a Thai 🇹🇭 & China 🇨🇳 & English 🇬🇧 large language model with 7 billion parameters, and it is Instruction finetune based on OpenThaiLLM-Prebuilt. It demonstrates competitive performance with Openthaigpt1.5-7b-instruct, and its optimized for application use cases, Retrieval-Augmented Generation (RAG), constrained generation, and reasoning tasks.

Model Detail

For release notes, please see our blog.
The detail about Text LLM part in this blog.

Datasets ratio

Requirements

The code of Qwen2.5 has been in the latest Hugging face transformers and we advise you to use the latest version of transformers.

With transformers<4.37.0, you will encounter the following error:

KeyError: 'qwen2'

Support Community

https://discord.gg/3WJwJjZt7r

Implementation

Here is a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate content.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "nectec/Pathumma-llm-text-1.0.0",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("nectec/Pathumma-llm-text-1.0.0")

prompt = "บริษัท A มีต้นทุนคงที่ 100,000 บาท และต้นทุนผันแปรต่อหน่วย 50 บาท ขายสินค้าได้ในราคา 150 บาทต่อหน่วย ต้องขายสินค้าอย่างน้อยกี่หน่วยเพื่อให้ถึงจุดคุ้มทุน?"
messages = [
    {"role": "system", "content": "You are Pathumma LLM, created by NECTEC. Your are a helpful assistant."},
    {"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)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=4096,
    repetition_penalty=1.1,
    temperature = 0.4
)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Implementation for GGUF

%pip install --quiet https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.90-cu124/llama_cpp_python-0.2.90-cp310-cp310-linux_x86_64.whl
import transformers
import torch
from llama_cpp import Llama
import os
import requests

local_dir = "your local dir"
directory_path = r'{local_dir}/Pathumma-llm-text-1.0.0'
if not os.path.exists(directory_path):
    os.mkdir(directory_path)

if not os.path.exists(f'{local_dir}/Pathumma-llm-text-1.0.0/Pathumma-llm-it-7b-Q4_K_M.gguf'):
    !wget -O f'{local_dir}/Pathumma-llm-text-1.0.0/Pathumma-llm-it-7b-Q4_K_M.gguf' "https://huggingface.co/nectec/Pathumma-llm-text-1.0.0/resolve/main/Pathumma-llm-it-7b-Q4_K_M.gguf?download=true"

# Initialize the Llama model
llm = Llama(model_path=f'{local_dir}/Pathumma-llm-text-1.0.0/Pathumma-llm-it-7b-Q4_K_M.gguf', n_gpu_layers=-1, n_ctx=8192,verbose=False)
tokenizer = transformers.AutoTokenizer.from_pretrained("nectec/Pathumma-llm-text-1.0.0")

memory = [{'content': 'You are Pathumma LLM, created by NECTEC (National Electronics and Computer Technology Center). Your are a helpful assistant.', 'role': 'system'},]

def generate(instuction,memory=memory):
    memory.append({'content': instuction, 'role': 'user'})
    p = tokenizer.apply_chat_template(
        memory,
        tokenize=False,
        add_generation_prompt=True
    )
    response = llm(
        p,
        max_tokens=2048,
        temperature=0.2,
        top_p=0.95,
        repeat_penalty=1.1,
        top_k=40,
        min_p=0.05,
        stop=["<|im_end|>"]
    )
    output = response['choices'][0]['text']
    memory.append({'content': output, 'role': 'assistant'})
    return output

print(generate("คุณคือใคร"))

Evaluation Performance

Model	m3exam	thaiexam	xcopa	belebele	xnli	thaisentiment	XL sum	flores200 eng > th	flores200 th > eng	iapp	AVG(NLU)	AVG(MC)	AVG(NLG)
Pathumma-llm-text-1.0.0	55.02	51.32	83	77.77	40.11	41.29	16.9286253	26.54	51.88	41.28	60.54	53.17	34.16
Openthaigpt1.5-7b-instruct	54.01	52.04	85.4	79.44	39.7	50.24	18.11	29.09	29.58	32.49	63.70	53.03	27.32
SeaLLMs-v3-7B-Chat	51.43	51.33	83.4	78.22	34.05	39.57	20.27	32.91	28.8	48.12	58.81	51.38	32.53
llama-3-typhoon-v1.5-8B	43.82	41.95	81.6	71.89	33.35	38.45	16.66	31.94	28.86	54.78	56.32	42.89	33.06
Meta-Llama-3.1-8B-Instruct	45.11	43.89	73.4	74.89	33.49	45.45	21.61	30.45	32.28	68.57	56.81	44.50	38.23

Contributor Contract

LLM Team
Pakawat Phasook (pakawat.phas@kmutt.ac.th)
Jessada Pranee (jessada.pran@kmutt.ac.th)
Arnon Saeoung (anon.saeoueng@gmail.com)
Kun Kerdthaisong (kun.ker@dome.tu.ac.th)
Kittisak Sukhantharat (kittisak.suk@stu.nida.ac.th)
Piyawat Chuangkrud (piyawat@it.kmitl.ac.th)
Chaianun Damrongrat (chaianun.damrongrat@nectec.or.th)
Sarawoot Kongyoung (sarawoot.kongyoung@nectec.or.th)

Audio Team
Pattara Tipaksorn (pattara.tip@ncr.nstda.or.th)
Wayupuk Sommuang (wayupuk.som@dome.tu.ac.th)
Oatsada Chatthong (atsada.cha@dome.tu.ac.th)
Kwanchiva Thangthai (kwanchiva.thangthai@nectec.or.th)

Vision Team
Thirawarit Pitiphiphat (60010474@kmitl.ac.th)
Peerapas Ngokpon (jamesselmon78169@gmail.com)
Theerasit Issaranon (theerasit.issaranon@nectec.or.th)

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen2.5,
    title = {Qwen2.5: A Party of Foundation Models},
    url = {https://qwenlm.github.io/blog/qwen2.5/},
    author = {Qwen Team},
    month = {September},
    year = {2024}
}

@article{qwen2,
      title={Qwen2 Technical Report}, 
      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
      journal={arXiv preprint arXiv:2407.10671},
      year={2024}
}