Transformers
GGUF
English
shining-valiant
shining-valiant-3
valiant
valiant-labs
qwen
qwen-3
qwen-3-1.7b
1.7b
reasoning
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code-reasoning
science
science-reasoning
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chemistry
earth-science
astronomy
machine-learning
artificial-intelligence
compsci
computer-science
information-theory
ML-Ops
math
cuda
deep-learning
agentic
LLM
neuromorphic
self-improvement
complex-systems
cognition
linguistics
philosophy
logic
epistemology
simulation
game-theory
knowledge-management
creativity
problem-solving
architect
engineer
developer
creative
analytical
expert
rationality
conversational
chat
instruct
imatrix
Instructions to use mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF", dtype="auto") - llama-cpp-python
How to use mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF", filename="Qwen3-1.7B-ShiningValiant3.i1-IQ1_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 mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF: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 mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF: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 mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF 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 mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF 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 mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF to start chatting
- Pi
How to use mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF: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": "mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF: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 mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-1.7B-ShiningValiant3-i1-GGUF-Q4_K_M
List all available models
lemonade list
auto-patch README.md
Browse files
README.md
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mradermacher:
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readme_rev: 1
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quantized_by: mradermacher
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---
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## About
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mradermacher:
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readme_rev: 1
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quantized_by: mradermacher
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tags:
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- shining-valiant
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- shining-valiant-3
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- valiant
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- valiant-labs
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- qwen
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- qwen-3
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- qwen-3-1.7b
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- 1.7b
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- reasoning
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- code
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- code-reasoning
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- science
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- science-reasoning
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- physics
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- biology
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- chemistry
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- earth-science
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- astronomy
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- machine-learning
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- artificial-intelligence
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- compsci
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- computer-science
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- information-theory
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- ML-Ops
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- math
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- cuda
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- deep-learning
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- transformers
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- agentic
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- LLM
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- neuromorphic
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- self-improvement
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- complex-systems
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- cognition
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- linguistics
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- philosophy
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- logic
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- epistemology
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- simulation
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- game-theory
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- knowledge-management
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- creativity
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- problem-solving
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- architect
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- engineer
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- developer
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- creative
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- analytical
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- expert
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- rationality
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- conversational
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- chat
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- instruct
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---
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## About
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