Instructions to use Limbicnation/qwen2-5-7b-image-prompt-lora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Limbicnation/qwen2-5-7b-image-prompt-lora-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Limbicnation/qwen2-5-7b-image-prompt-lora-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Limbicnation/qwen2-5-7b-image-prompt-lora-v2 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 Limbicnation/qwen2-5-7b-image-prompt-lora-v2 # Run inference directly in the terminal: llama cli -hf Limbicnation/qwen2-5-7b-image-prompt-lora-v2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Limbicnation/qwen2-5-7b-image-prompt-lora-v2 # Run inference directly in the terminal: llama cli -hf Limbicnation/qwen2-5-7b-image-prompt-lora-v2
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 Limbicnation/qwen2-5-7b-image-prompt-lora-v2 # Run inference directly in the terminal: ./llama-cli -hf Limbicnation/qwen2-5-7b-image-prompt-lora-v2
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 Limbicnation/qwen2-5-7b-image-prompt-lora-v2 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Limbicnation/qwen2-5-7b-image-prompt-lora-v2
Use Docker
docker model run hf.co/Limbicnation/qwen2-5-7b-image-prompt-lora-v2
- LM Studio
- Jan
- Ollama
How to use Limbicnation/qwen2-5-7b-image-prompt-lora-v2 with Ollama:
ollama run hf.co/Limbicnation/qwen2-5-7b-image-prompt-lora-v2
- Unsloth Desktop
- Pi
How to use Limbicnation/qwen2-5-7b-image-prompt-lora-v2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Limbicnation/qwen2-5-7b-image-prompt-lora-v2
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": "Limbicnation/qwen2-5-7b-image-prompt-lora-v2" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Limbicnation/qwen2-5-7b-image-prompt-lora-v2 with Docker Model Runner:
docker model run hf.co/Limbicnation/qwen2-5-7b-image-prompt-lora-v2
- Lemonade
How to use Limbicnation/qwen2-5-7b-image-prompt-lora-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Limbicnation/qwen2-5-7b-image-prompt-lora-v2
Run and chat with the model
lemonade run user.qwen2-5-7b-image-prompt-lora-v2-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use Limbicnation/qwen2-5-7b-image-prompt-lora-v2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Limbicnation/qwen2-5-7b-image-prompt-lora-v2
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 Limbicnation/qwen2-5-7b-image-prompt-lora-v2
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Limbicnation/qwen2-5-7b-image-prompt-lora-v2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Limbicnation/qwen2-5-7b-image-prompt-lora-v2
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 "Limbicnation/qwen2-5-7b-image-prompt-lora-v2" \ --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"
Model Card for qwen2-5-7b-image-prompt-lora-v2
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct. It has been trained using TRL.
Quick start
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Limbicnation/qwen2-5-7b-image-prompt-lora-v2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Training procedure
This model was trained with SFT.
Framework versions
- TRL: 0.27.1
- Transformers: 4.57.6
- Pytorch: 2.6.0+cu124
- Datasets: 4.5.0
- Tokenizers: 0.22.2
Citations
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
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