Instructions to use Phonepadith/aidc-laollm-5k-based-qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Phonepadith/aidc-laollm-5k-based-qwen with fastai:

from huggingface_hub import from_pretrained_fastai

learn = from_pretrained_fastai("Phonepadith/aidc-laollm-5k-based-qwen")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Phonepadith/aidc-laollm-5k-based-qwen 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 Phonepadith/aidc-laollm-5k-based-qwen
# Run inference directly in the terminal:
llama cli -hf Phonepadith/aidc-laollm-5k-based-qwen

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Phonepadith/aidc-laollm-5k-based-qwen
# Run inference directly in the terminal:
llama cli -hf Phonepadith/aidc-laollm-5k-based-qwen

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 Phonepadith/aidc-laollm-5k-based-qwen
# Run inference directly in the terminal:
./llama-cli -hf Phonepadith/aidc-laollm-5k-based-qwen

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 Phonepadith/aidc-laollm-5k-based-qwen
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Phonepadith/aidc-laollm-5k-based-qwen

Use Docker

docker model run hf.co/Phonepadith/aidc-laollm-5k-based-qwen

LM Studio
Jan

vLLM

How to use Phonepadith/aidc-laollm-5k-based-qwen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Phonepadith/aidc-laollm-5k-based-qwen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phonepadith/aidc-laollm-5k-based-qwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Phonepadith/aidc-laollm-5k-based-qwen

Ollama
How to use Phonepadith/aidc-laollm-5k-based-qwen with Ollama:
```
ollama run hf.co/Phonepadith/aidc-laollm-5k-based-qwen
```

Unsloth Studio

How to use Phonepadith/aidc-laollm-5k-based-qwen 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 Phonepadith/aidc-laollm-5k-based-qwen 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 Phonepadith/aidc-laollm-5k-based-qwen to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Phonepadith/aidc-laollm-5k-based-qwen to start chatting

Atomic Chat new
Docker Model Runner
How to use Phonepadith/aidc-laollm-5k-based-qwen with Docker Model Runner:
```
docker model run hf.co/Phonepadith/aidc-laollm-5k-based-qwen
```

Lemonade

How to use Phonepadith/aidc-laollm-5k-based-qwen with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Phonepadith/aidc-laollm-5k-based-qwen

Run and chat with the model

lemonade run user.aidc-laollm-5k-based-qwen-{{QUANT_TAG}}

List all available models

lemonade list

A newer version of this model is available: Phonepadith/Laollm

🧠 AIDC LLM ສຳລັບພາສາລາວ - ແລະ ສະຫລຸບຂໍ້ມູນ — Fine-tuned on AIDC-5K (Q8 GGUF)

This is a Lao language chat model based on Qwen/Qwen1.5-7B, fine-tuned on the AIDC 5K Lao prompt-completion dataset, and exported to GGUF format with Q8_0 quantization for fast inference using llama.cpp, LM Studio, or Ollama.

🧾 Model Summary

Feature	Details
Base Model	Qwen1.5-7B
Fine-tuned By	Phonepadith Phoummavong
Language	Lao (lo)
Dataset	AIDC 5K Lao Prompt-Completion Dataset
Quantization	Q8_0 (8-bit, GGUF)
Format	GGUF
File Name	`seallm-7b-lao-finetuned-lora-q8.gguf`
Size (est.)	~3–5 GB
License	apache-2.0

💡 Use Cases

Lao chatbots or digital assistants
Cultural and educational tools in Lao
Research on low-resource language modeling
Lao-native prompt generation and dialogue completion

📥 How to Use

🔸 LM Studio

Download the .gguf model file.
Open LM Studio.
Click "Add Local Model".
Load aidc-laollm-5k-based-qwen-Q8.gguf.

🔸 Ollama

Place the .gguf file in your models directory.

Create a Modelfile:

FROM qwen:7b
PARAMETER quantization=q8_0

Downloads last month: 10

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for Phonepadith/aidc-laollm-5k-based-qwen

Base model

Qwen/Qwen2.5-VL-7B-Instruct

Quantized

(147)

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