Instructions to use jeiku/Konocchini-7B_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeiku/Konocchini-7B_GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jeiku/Konocchini-7B_GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use jeiku/Konocchini-7B_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 jeiku/Konocchini-7B_GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf jeiku/Konocchini-7B_GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jeiku/Konocchini-7B_GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf jeiku/Konocchini-7B_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 jeiku/Konocchini-7B_GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf jeiku/Konocchini-7B_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 jeiku/Konocchini-7B_GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf jeiku/Konocchini-7B_GGUF:Q2_K
Use Docker
docker model run hf.co/jeiku/Konocchini-7B_GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use jeiku/Konocchini-7B_GGUF with Ollama:
ollama run hf.co/jeiku/Konocchini-7B_GGUF:Q2_K
- Unsloth Studio
How to use jeiku/Konocchini-7B_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 jeiku/Konocchini-7B_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 jeiku/Konocchini-7B_GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jeiku/Konocchini-7B_GGUF to start chatting
- Docker Model Runner
How to use jeiku/Konocchini-7B_GGUF with Docker Model Runner:
docker model run hf.co/jeiku/Konocchini-7B_GGUF:Q2_K
- Lemonade
How to use jeiku/Konocchini-7B_GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jeiku/Konocchini-7B_GGUF:Q2_K
Run and chat with the model
lemonade run user.Konocchini-7B_GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
This is a merge created by https://huggingface.co/Test157t I have merely quantized the model into GGUF. Please visit https://huggingface.co/Test157t/Kunocchini-7b for the original weights. The original description is as follows:
Thanks to @Epiculous for the dope model/ help with llm backends and support overall.
Id like to also thank @kalomaze for the dope sampler additions to ST.
@SanjiWatsuki Thank you very much for the help, and the model!
ST users can find the TextGenPreset in the folder labeled so.
Quants:Thank you @bartowski! https://huggingface.co/bartowski/Kunocchini-exl2
mergedmodel
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [0, 32]
- model: Epiculous/Fett-uccine-7B
layer_range: [0, 32]
merge_method: slerp
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
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