Instructions to use mradermacher/karakuri-lm-70b-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/karakuri-lm-70b-v0.1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/karakuri-lm-70b-v0.1-GGUF", dtype="auto") - llama-cpp-python
How to use mradermacher/karakuri-lm-70b-v0.1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/karakuri-lm-70b-v0.1-GGUF", filename="karakuri-lm-70b-v0.1.IQ4_XS.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/karakuri-lm-70b-v0.1-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/karakuri-lm-70b-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/karakuri-lm-70b-v0.1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/karakuri-lm-70b-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/karakuri-lm-70b-v0.1-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/karakuri-lm-70b-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/karakuri-lm-70b-v0.1-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/karakuri-lm-70b-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/karakuri-lm-70b-v0.1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/karakuri-lm-70b-v0.1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/karakuri-lm-70b-v0.1-GGUF with Ollama:
ollama run hf.co/mradermacher/karakuri-lm-70b-v0.1-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/karakuri-lm-70b-v0.1-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/karakuri-lm-70b-v0.1-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/karakuri-lm-70b-v0.1-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/karakuri-lm-70b-v0.1-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use mradermacher/karakuri-lm-70b-v0.1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/karakuri-lm-70b-v0.1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/karakuri-lm-70b-v0.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/karakuri-lm-70b-v0.1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.karakuri-lm-70b-v0.1-GGUF-Q4_K_M
List all available models
lemonade list
auto-patch README.md
Browse files
README.md
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static quants of https://huggingface.co/karakuri-ai/karakuri-lm-70b-v0.1
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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/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q3_K_S.gguf) | Q3_K_S | 30.2 | |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q3_K_M.gguf) | Q3_K_M | 33.5 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q3_K_L.gguf) | Q3_K_L | 36.4 | |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q4_K_S.gguf) | Q4_K_S | 39.5 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q4_K_M.gguf) | Q4_K_M | 41.7 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q5_K_S.gguf) | Q5_K_S | 47.7 | |
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static quants of https://huggingface.co/karakuri-ai/karakuri-lm-70b-v0.1
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<!-- provided-files -->
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-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/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q3_K_S.gguf) | Q3_K_S | 30.2 | |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q3_K_M.gguf) | Q3_K_M | 33.5 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q3_K_L.gguf) | Q3_K_L | 36.4 | |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.IQ4_XS.gguf) | IQ4_XS | 37.4 | |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q4_K_S.gguf) | Q4_K_S | 39.5 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q4_K_M.gguf) | Q4_K_M | 41.7 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-70b-v0.1-GGUF/resolve/main/karakuri-lm-70b-v0.1.Q5_K_S.gguf) | Q5_K_S | 47.7 | |
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