Instructions to use UdayGattu23/qwen2.5-7b-finetuned-argus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UdayGattu23/qwen2.5-7b-finetuned-argus with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="UdayGattu23/qwen2.5-7b-finetuned-argus",
	filename="gguf/qwen2.5-7b-finetuned-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use UdayGattu23/qwen2.5-7b-finetuned-argus 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 UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf UdayGattu23/qwen2.5-7b-finetuned-argus: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 UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf UdayGattu23/qwen2.5-7b-finetuned-argus: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 UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M

Use Docker

docker model run hf.co/UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M

LM Studio
Jan

vLLM

How to use UdayGattu23/qwen2.5-7b-finetuned-argus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UdayGattu23/qwen2.5-7b-finetuned-argus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UdayGattu23/qwen2.5-7b-finetuned-argus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M

Ollama
How to use UdayGattu23/qwen2.5-7b-finetuned-argus with Ollama:
```
ollama run hf.co/UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M
```

Unsloth Studio

How to use UdayGattu23/qwen2.5-7b-finetuned-argus 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 UdayGattu23/qwen2.5-7b-finetuned-argus 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 UdayGattu23/qwen2.5-7b-finetuned-argus to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for UdayGattu23/qwen2.5-7b-finetuned-argus to start chatting

How to use UdayGattu23/qwen2.5-7b-finetuned-argus with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf UdayGattu23/qwen2.5-7b-finetuned-argus: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": "UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use UdayGattu23/qwen2.5-7b-finetuned-argus with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf UdayGattu23/qwen2.5-7b-finetuned-argus: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 UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use UdayGattu23/qwen2.5-7b-finetuned-argus with Docker Model Runner:
```
docker model run hf.co/UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M
```

Lemonade

How to use UdayGattu23/qwen2.5-7b-finetuned-argus with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull UdayGattu23/qwen2.5-7b-finetuned-argus:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-7b-finetuned-argus-Q4_K_M

List all available models

lemonade list

Qwen2.5-7B Finetuned on Argus Dataset

This model is a finetuned version of Qwen2.5-7B using LoRA with rank 128.

Training Details

Base Model: Qwen/Qwen2.5-7B
Training Method: LoRA (rank=128, alpha=256)
Dataset: 27,997 text samples
Epochs: 2 (best checkpoint from epoch 1)
Batch Size: 16 (effective)
Learning Rate: 5e-5
Hardware: A100 GPU

Training Results

Epoch 1: Training Loss: 1.301, Validation Loss: 1.589 (best)
Epoch 2: Training Loss: 1.699, Validation Loss: 1.826

Available Formats

PyTorch: Original model weights
GGUF: Multiple quantization levels available
- Q8_0: Highest quality (7.5GB)
- Q6_K: Very high quality (5.5GB)
- Q5_K_M: High quality (4.8GB)
- Q4_K_M: Good quality (3.8GB)
- Q4_0: Acceptable quality (3.5GB)

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("UdayGattu23/qwen2.5-7b-finetuned-argus")
tokenizer = AutoTokenizer.from_pretrained("UdayGattu23/qwen2.5-7b-finetuned-argus")

With llama.cpp (GGUF)

./main -m qwen2.5-7b-finetuned-Q4_K_M.gguf -p "Your prompt here"

License

Apache 2.0

Downloads last month: 26

Safetensors

Model size

8B params

Tensor type

F16

Model tree for UdayGattu23/qwen2.5-7b-finetuned-argus

Base model

Qwen/Qwen2.5-7B

Quantized

(83)

this model