Instructions to use fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF", dtype="auto") - llama-cpp-python
How to use fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF", filename="huihui-granite-4.0-h-tiny-abliterated-q4_k_m-imat.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 fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-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 fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-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 fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-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 fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-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 fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF with Ollama:
ollama run hf.co/fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-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 fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-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 fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
Update README.md
Browse files
README.md
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datasets:
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- IntelligentEstate/The_Key
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# IntelligentEstate/Jari-7B-Q4_K_M-GGUF
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Invoke the llama.cpp server or the CLI.
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### CLI:
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```bash
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llama-cli --hf-repo fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF --hf-file huihui-granite-4.0-h-tiny-abliterated-q4_k_m-imat.gguf -p "The meaning to life and the universe is"
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```
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### Server:
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```bash
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llama-server --hf-repo fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF --hf-file huihui-granite-4.0-h-tiny-abliterated-q4_k_m-imat.gguf -c 2048
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```
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
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Step 1: Clone llama.cpp from GitHub.
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git clone https://github.com/ggerganov/llama.cpp
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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cd llama.cpp && LLAMA_CURL=1 make
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Step 3: Run inference through the main binary.
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```
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./llama-cli --hf-repo fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF --hf-file huihui-granite-4.0-h-tiny-abliterated-q4_k_m-imat.gguf -p "The meaning to life and the universe is"
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```
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or
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```
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./llama-server --hf-repo fuzzy-mittenz/Huihui-granite-4.0-h-tiny-abliterated-Q4_K_M-GGUF --hf-file huihui-granite-4.0-h-tiny-abliterated-q4_k_m-imat.gguf -c 2048
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```
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datasets:
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- IntelligentEstate/The_Key
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..untested.. only available for evaluation
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# IntelligentEstate/Jari-7B-Q4_K_M-GGUF
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```
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Invoke the llama.cpp server or the CLI.
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