Instructions to use qaitest/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qaitest/test with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="qaitest/test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("qaitest/test", dtype="auto")

llama-cpp-python

How to use qaitest/test with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="qaitest/test",
	filename="Llama-3.2-1B-Instruct-Q3_K_L.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 qaitest/test with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf qaitest/test:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf qaitest/test:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf qaitest/test:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf qaitest/test: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 qaitest/test:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf qaitest/test: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 qaitest/test:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf qaitest/test:Q4_K_M

Use Docker

docker model run hf.co/qaitest/test:Q4_K_M

LM Studio
Jan

vLLM

How to use qaitest/test with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/qaitest/test:Q4_K_M

SGLang

How to use qaitest/test with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "qaitest/test" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qaitest/test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "qaitest/test" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qaitest/test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use qaitest/test with Ollama:
```
ollama run hf.co/qaitest/test:Q4_K_M
```

Unsloth Studio new

How to use qaitest/test 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 qaitest/test 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 qaitest/test to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for qaitest/test to start chatting

Docker Model Runner
How to use qaitest/test with Docker Model Runner:
```
docker model run hf.co/qaitest/test:Q4_K_M
```

Lemonade

How to use qaitest/test with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull qaitest/test:Q4_K_M

Run and chat with the model

lemonade run user.test-Q4_K_M

List all available models

lemonade list

💫 Community Model> Llama 3.2 1B Instruct by Meta-Llama

👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord.

Model creator: meta-llama
Original model: Llama-3.2-1B-Instruct
GGUF quantization: provided by bartowski based on llama.cpp release b3821

Technical Details

Llama 3.2 is optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks.

Officially supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai languages, but is trained on even more.

128K context length support

Special thanks

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

Disclaimers

LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

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GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

6-bit

8-bit

Model tree for qaitest/test

Base model

meta-llama/Llama-3.2-1B-Instruct

Quantized

(375)

this model