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

Update README.md

by Pakawat-Phasook - opened Oct 24, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+11

-10

Files changed (1) hide show

README.md +11 -10

README.md CHANGED Viewed

@@ -36,7 +36,7 @@ KeyError: 'qwen2'
 ```
 ## **Support Community**
-**https://discord.gg/JsezgAf7**
 ## **Implementation**
@@ -55,7 +55,7 @@ tokenizer = AutoTokenizer.from_pretrained("nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Be
 prompt = "บริษัท A มีต้นทุนคงที่ 100,000 บาท และต้นทุนผันแปรต่อหน่วย 50 บาท ขายสินค้าได้ในราคา 150 บาทต่อหน่วย ต้องขายสินค้าอย่างน้อยกี่หน่วยเพื่อให้ถึงจุดคุ้มทุน?"
 messages = [
-    {"role": "system", "content": "คุณคือ DoodNiLT Assistant จงตอบคำถามอธิบายเป็นภาษาไทย"},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
@@ -68,20 +68,21 @@ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 generated_ids = model.generate(
     model_inputs.input_ids,
     max_new_tokens=4096,
-    repetition_penalty=1.2
 )
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```
 ## **Evaluation Performance**
-| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | MMLU | M3Exam | M6Exam |
-| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| OpenthaiLLM-Prebuilt-7B | **0.5493** | 0.6315 | **0.6307** | **0.4655** | **0.37** | **0.5294** | **0.7054** | **0.5705** | **0.596** |
-| SeaLLM-v3-7B | 0.4753 | **0.6421** | 0.6153 | 0.3275 | 0.3464 | 0.4813 | 0.7037 | 0.4907 | 0.4625 | 0.3666 |
-| llama-3-typhoon-v1.5-8B | 0.3765 | 0.3473 | 0.5538 | 0.4137 | 0.2913 | 0.3965 | 0.6451 | 0.4312 | 0.4125 |
-| Qwen-2-7B | 0.4814 | 0.621 | 0.6153 | 0.3448 | 0.3385 | 0.4802 | 0.7073 | 0.4949 | 0.4807 |
-| Meta-Llama-3.1-8B | 0.3641 | 0.2631 | 0.2769 | 0.3793 | 0.1811 | 0.2929 | 0.6591 | 0.4239 | 0.3583 |
 ## **Citation**

 ```
 ## **Support Community**
+**https://discord.gg/3WJwJjZt7r**
 ## **Implementation**
 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(
 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)
 ```
 ## **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 |
+| 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 |
 ## **Citation**