Instructions to use radi04/Qwen3-8B-Deview-Finetune-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radi04/Qwen3-8B-Deview-Finetune-v3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("radi04/Qwen3-8B-Deview-Finetune-v3", dtype="auto") - llama-cpp-python
How to use radi04/Qwen3-8B-Deview-Finetune-v3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="radi04/Qwen3-8B-Deview-Finetune-v3", filename="Qwen3-8B-Deview-Finetune-v3-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use radi04/Qwen3-8B-Deview-Finetune-v3 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M # Run inference directly in the terminal: llama-cli -hf radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M # Run inference directly in the terminal: llama-cli -hf radi04/Qwen3-8B-Deview-Finetune-v3: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 radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf radi04/Qwen3-8B-Deview-Finetune-v3: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 radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M
Use Docker
docker model run hf.co/radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use radi04/Qwen3-8B-Deview-Finetune-v3 with Ollama:
ollama run hf.co/radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M
- Unsloth Studio
How to use radi04/Qwen3-8B-Deview-Finetune-v3 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 radi04/Qwen3-8B-Deview-Finetune-v3 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 radi04/Qwen3-8B-Deview-Finetune-v3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for radi04/Qwen3-8B-Deview-Finetune-v3 to start chatting
- Pi
How to use radi04/Qwen3-8B-Deview-Finetune-v3 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf radi04/Qwen3-8B-Deview-Finetune-v3: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": "radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use radi04/Qwen3-8B-Deview-Finetune-v3 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf radi04/Qwen3-8B-Deview-Finetune-v3: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 radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use radi04/Qwen3-8B-Deview-Finetune-v3 with Docker Model Runner:
docker model run hf.co/radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M
- Lemonade
How to use radi04/Qwen3-8B-Deview-Finetune-v3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull radi04/Qwen3-8B-Deview-Finetune-v3:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-8B-Deview-Finetune-v3-Q4_K_M
List all available models
lemonade list
Model Card for Qwen3-8B-Deview-Finetune-v3
This model is a fine-tuned version of Qwen/Qwen3-8B. 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="radi04/Qwen3-8B-Deview-Finetune-v3", 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: 0.25.1
- Transformers: 4.57.1
- Pytorch: 2.9.1
- Datasets: 4.4.1
- Tokenizers: 0.22.1
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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