Instructions to use methil-group/nexus-flash-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use methil-group/nexus-flash-9B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("methil-group/nexus-flash-9B-GGUF", dtype="auto") - llama-cpp-python
How to use methil-group/nexus-flash-9B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="methil-group/nexus-flash-9B-GGUF", filename="nexus-flash-9B.BF16.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 methil-group/nexus-flash-9B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf methil-group/nexus-flash-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf methil-group/nexus-flash-9B-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 methil-group/nexus-flash-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf methil-group/nexus-flash-9B-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 methil-group/nexus-flash-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf methil-group/nexus-flash-9B-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 methil-group/nexus-flash-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf methil-group/nexus-flash-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/methil-group/nexus-flash-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use methil-group/nexus-flash-9B-GGUF with Ollama:
ollama run hf.co/methil-group/nexus-flash-9B-GGUF:Q4_K_M
- Unsloth Studio
How to use methil-group/nexus-flash-9B-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 methil-group/nexus-flash-9B-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 methil-group/nexus-flash-9B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for methil-group/nexus-flash-9B-GGUF to start chatting
- Pi
How to use methil-group/nexus-flash-9B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf methil-group/nexus-flash-9B-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": "methil-group/nexus-flash-9B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use methil-group/nexus-flash-9B-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 methil-group/nexus-flash-9B-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 methil-group/nexus-flash-9B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use methil-group/nexus-flash-9B-GGUF with Docker Model Runner:
docker model run hf.co/methil-group/nexus-flash-9B-GGUF:Q4_K_M
- Lemonade
How to use methil-group/nexus-flash-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull methil-group/nexus-flash-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.nexus-flash-9B-GGUF-Q4_K_M
List all available models
lemonade list
Nexus-Flash-9B-GGUF
This model is a fine-tuned version of unsloth/Qwen3.5-9B, optimized for agent-based reasoning tasks. It was trained using the Unsloth framework to achieve faster training speeds and memory efficiency.
📋 Model Details
- Developed by: ethanzxv
- Base Model: unsloth/Qwen3.5-9B
- License: Apache-2.0
- Language: English
- Finetuning Dataset: lambda/hermes-agent-reasoning-traces
🚀 Training & Optimization
This model was trained 2x faster using Unsloth combined with Hugging Face's TRL library. Unsloth allows for efficient fine-tuning of Large Language Models (LLMs) with significantly reduced VRAM usage and increased throughput.
Dataset Information
The model was fine-tuned on the Hermes Agent Reasoning Traces dataset. This dataset focuses on enhancing the model's ability to perform complex reasoning steps, particularly in agentic workflows, by providing detailed traces of thought processes and decision-making paths.
- Dataset: lambda/hermes-agent-reasoning-traces
🎯 Intended Use & Capabilities
This model is designed for:
- Agent Reasoning: Improved performance in tasks requiring multi-step logical deduction.
- Complex Problem Solving: Better handling of intricate queries that require chain-of-thought processing.
- General Text Generation: Maintains the strong general capabilities of the base Qwen3.5-9B model.
📄 License
This model is released under the Apache-2.0 license. Please refer to the base model's license and the dataset's license for any additional restrictions or requirements.
🙏 Acknowledgements
- Unsloth: For providing the efficient fine-tuning framework.
- Hugging Face TRL: For the training reinforcement library.
- Lambda: For curating the Hermes Agent Reasoning Traces dataset.
- Alibaba Cloud: For the original Qwen3.5 base model.
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