Instructions to use mradermacher/Endurance-100B-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Endurance-100B-v1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Endurance-100B-v1-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/Endurance-100B-v1-GGUF 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 mradermacher/Endurance-100B-v1-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf mradermacher/Endurance-100B-v1-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/Endurance-100B-v1-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf mradermacher/Endurance-100B-v1-GGUF:Q2_K
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/Endurance-100B-v1-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Endurance-100B-v1-GGUF:Q2_K
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/Endurance-100B-v1-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Endurance-100B-v1-GGUF:Q2_K
Use Docker
docker model run hf.co/mradermacher/Endurance-100B-v1-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use mradermacher/Endurance-100B-v1-GGUF with Ollama:
ollama run hf.co/mradermacher/Endurance-100B-v1-GGUF:Q2_K
- Unsloth Studio
How to use mradermacher/Endurance-100B-v1-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/Endurance-100B-v1-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/Endurance-100B-v1-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/Endurance-100B-v1-GGUF to start chatting
- Pi
How to use mradermacher/Endurance-100B-v1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/Endurance-100B-v1-GGUF:Q2_K
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/Endurance-100B-v1-GGUF:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use mradermacher/Endurance-100B-v1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Endurance-100B-v1-GGUF:Q2_K
- Lemonade
How to use mradermacher/Endurance-100B-v1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Endurance-100B-v1-GGUF:Q2_K
Run and chat with the model
lemonade run user.Endurance-100B-v1-GGUF-Q2_K
List all available models
lemonade list
- Hermes Agent
How to use mradermacher/Endurance-100B-v1-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/Endurance-100B-v1-GGUF:Q2_K
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/Endurance-100B-v1-GGUF:Q2_K
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mradermacher/Endurance-100B-v1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/Endurance-100B-v1-GGUF:Q2_K
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 "mradermacher/Endurance-100B-v1-GGUF:Q2_K" \ --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"
auto-patch README.md
Browse files
README.md
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static quants of https://huggingface.co/TheDrummer/Endurance-100B-v1
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<!-- provided-files -->
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weighted/imatrix quants
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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| [GGUF](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q3_K_S.gguf) | Q3_K_S | 43.4 | |
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| [GGUF](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q3_K_M.gguf) | Q3_K_M | 48.5 | lower quality |
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| [PART 1](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q3_K_L.gguf.part2of2) | Q3_K_L | 53.0 | |
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| [PART 1](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q4_K_S.gguf.part2of2) | Q4_K_S | 57.1 | fast, recommended |
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| [PART 1](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q4_K_M.gguf.part2of2) | Q4_K_M | 60.1 | fast, recommended |
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| [PART 1](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q5_K_S.gguf.part2of2) | Q5_K_S | 69.2 | |
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static quants of https://huggingface.co/TheDrummer/Endurance-100B-v1
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<!-- provided-files -->
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Endurance-100B-v1-i1-GGUF
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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| [GGUF](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q3_K_S.gguf) | Q3_K_S | 43.4 | |
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| [GGUF](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q3_K_M.gguf) | Q3_K_M | 48.5 | lower quality |
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| [PART 1](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q3_K_L.gguf.part2of2) | Q3_K_L | 53.0 | |
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| [PART 1](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.IQ4_XS.gguf.part2of2) | IQ4_XS | 54.2 | |
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| [PART 1](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q4_K_S.gguf.part2of2) | Q4_K_S | 57.1 | fast, recommended |
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| [PART 1](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q4_K_M.gguf.part2of2) | Q4_K_M | 60.1 | fast, recommended |
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| [PART 1](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Endurance-100B-v1-GGUF/resolve/main/Endurance-100B-v1.Q5_K_S.gguf.part2of2) | Q5_K_S | 69.2 | |
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