Instructions to use juvereturn/Qwen3-1.7B-CS592-Final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use juvereturn/Qwen3-1.7B-CS592-Final with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use juvereturn/Qwen3-1.7B-CS592-Final 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 juvereturn/Qwen3-1.7B-CS592-Final:F16
# Run inference directly in the terminal:
llama cli -hf juvereturn/Qwen3-1.7B-CS592-Final:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf juvereturn/Qwen3-1.7B-CS592-Final:F16
# Run inference directly in the terminal:
llama cli -hf juvereturn/Qwen3-1.7B-CS592-Final:F16

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 juvereturn/Qwen3-1.7B-CS592-Final:F16
# Run inference directly in the terminal:
./llama-cli -hf juvereturn/Qwen3-1.7B-CS592-Final:F16

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 juvereturn/Qwen3-1.7B-CS592-Final:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf juvereturn/Qwen3-1.7B-CS592-Final:F16

Use Docker

docker model run hf.co/juvereturn/Qwen3-1.7B-CS592-Final:F16

LM Studio
Jan

vLLM

How to use juvereturn/Qwen3-1.7B-CS592-Final with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "juvereturn/Qwen3-1.7B-CS592-Final"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "juvereturn/Qwen3-1.7B-CS592-Final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/juvereturn/Qwen3-1.7B-CS592-Final:F16

Ollama
How to use juvereturn/Qwen3-1.7B-CS592-Final with Ollama:
```
ollama run hf.co/juvereturn/Qwen3-1.7B-CS592-Final:F16
```
Unsloth Desktop

How to use juvereturn/Qwen3-1.7B-CS592-Final with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf juvereturn/Qwen3-1.7B-CS592-Final:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "juvereturn/Qwen3-1.7B-CS592-Final:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use juvereturn/Qwen3-1.7B-CS592-Final with Docker Model Runner:
```
docker model run hf.co/juvereturn/Qwen3-1.7B-CS592-Final:F16
```

Lemonade

How to use juvereturn/Qwen3-1.7B-CS592-Final with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull juvereturn/Qwen3-1.7B-CS592-Final:F16

Run and chat with the model

lemonade run user.Qwen3-1.7B-CS592-Final-F16

List all available models

lemonade list

Hermes Agent

How to use juvereturn/Qwen3-1.7B-CS592-Final with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf juvereturn/Qwen3-1.7B-CS592-Final:F16

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 juvereturn/Qwen3-1.7B-CS592-Final:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use juvereturn/Qwen3-1.7B-CS592-Final with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf juvereturn/Qwen3-1.7B-CS592-Final:F16

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 "juvereturn/Qwen3-1.7B-CS592-Final:F16" \
  --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"

Qwen3-1.7B-CS592-Final

Model Description

Fine-tuned from Qwen/Qwen3-1.7B using QLoRA (4-bit) with supervised fine-tuning.

Training Details

Dataset: juvereturn/CS592-Final
LoRA rank: 16, alpha: 32
Epochs: 3, Learning rate: 0.0002

Intended Use

This model is a test model used for the CS-394/594 class at DigiPen.

The model is a Bible assistant that can answer people's questions about the Bible and help them feel better. I believe the Bible has the ability to heal everyone in the world.

Limitations

This model is a single-turn model and has not been trained on support long, multi-turn conversations.

Downloads last month: 20

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for juvereturn/Qwen3-1.7B-CS592-Final

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Adapter

(764)

this model