Text Generation
PyTorch
Safetensors
GGUF
Thai
Chinese
English
qwen2
chemistry
biology
finance
legal
code
medical
text-generation-inference
conversational
Instructions to use nectec/Pathumma-llm-text-1.0.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use nectec/Pathumma-llm-text-1.0.0 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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
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
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 serve -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
- OpenClaw new
How to use nectec/Pathumma-llm-text-1.0.0 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nectec/Pathumma-llm-text-1.0.0:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "nectec/Pathumma-llm-text-1.0.0:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- 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
- Hermes Agent
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 serve -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
- Atomic Chat
Create README.md
#1
by Pakawat-Phasook - opened
README.md
ADDED
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---
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license: apache-2.0
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language:
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- th
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- zh
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- en
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metrics:
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- accuracy
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| 9 |
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base_model:
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- Qwen/Qwen2.5-7B
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pipeline_tag: text-generation
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tags:
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- chemistry
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- biology
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- finance
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- legal
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- code
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- medical
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- text-generation-inference
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---
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# **OpenThaiLLM-Prebuilt-7B: Thai & China & English Large Language Model**
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**OpenThaiLLM-Prebuilt-7B** is a Thai 🇹🇭 & Chinese 🇨🇳 large language model with 7 billion parameters, and it is continue pretrain based on Qwen2-7B.
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It demonstrates competitive performance with llama-3-typhoon-v1.5-8b, and is optimized for application use cases, Retrieval-Augmented Generation (RAG),
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constrained generation, and reasoning tasks.
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For release notes, please see our [blog](https://medium.com/@superkingbasskb/openthaillm-prebuilt-release-f1b0e22be6a5).
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**We do not recommend using base language models for conversations.** Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
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## **Requirements**
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The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
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With `transformers<4.37.0`, you will encounter the following error:
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```
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KeyError: 'qwen2'
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```
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## **Support Community**
|
| 38 |
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|
| 39 |
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**https://discord.gg/JsezgAf7**
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## **Implementation**
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Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate content.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B-Instruct",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B-Instruct")
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prompt = "บริษัท A มีต้นทุนคงที่ 100,000 บาท และต้นทุนผันแปรต่อหน่วย 50 บาท ขายสินค้าได้ในราคา 150 บาทต่อหน่วย ต้องขายสินค้าอย่างน้อยกี่หน่วยเพื่อให้ถึงจุดคุ้มทุน?"
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messages = [
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{"role": "system", "content": "คุณคือ DoodNiLT Assistant จงตอบคำถามอธิบายเป็นภาษาไทย"},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=4096,
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repetition_penalty=1.2
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)
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## **Evaluation Performance**
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| 78 |
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| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | MMLU | M3Exam | M6Exam |
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| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| OpenthaiLLM-Prebuilt-7B | **0.5493** | 0.6315 | **0.6307** | **0.4655** | **0.37** | **0.5294** | **0.7054** | **0.5705** | **0.596** |
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| SeaLLM-v3-7B | 0.4753 | **0.6421** | 0.6153 | 0.3275 | 0.3464 | 0.4813 | 0.7037 | 0.4907 | 0.4625 | 0.3666 |
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| llama-3-typhoon-v1.5-8B | 0.3765 | 0.3473 | 0.5538 | 0.4137 | 0.2913 | 0.3965 | 0.6451 | 0.4312 | 0.4125 |
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| Qwen-2-7B | 0.4814 | 0.621 | 0.6153 | 0.3448 | 0.3385 | 0.4802 | 0.7073 | 0.4949 | 0.4807 |
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| Meta-Llama-3.1-8B | 0.3641 | 0.2631 | 0.2769 | 0.3793 | 0.1811 | 0.2929 | 0.6591 | 0.4239 | 0.3583 |
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## **Citation**
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| 88 |
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If you find our work helpful, feel free to give us a cite.
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| 90 |
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```
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@misc{qwen2.5,
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| 93 |
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title = {Qwen2.5: A Party of Foundation Models},
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| 94 |
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url = {https://qwenlm.github.io/blog/qwen2.5/},
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| 95 |
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author = {Qwen Team},
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| 96 |
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month = {September},
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| 97 |
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year = {2024}
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| 98 |
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}
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| 99 |
+
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| 100 |
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@article{qwen2,
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| 101 |
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title={Qwen2 Technical Report},
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| 102 |
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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},
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| 103 |
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journal={arXiv preprint arXiv:2407.10671},
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| 104 |
+
year={2024}
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| 105 |
+
}
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| 106 |
+
|