Instructions to use Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix 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 Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix: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 Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix: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 Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M
Use Docker
docker model run hf.co/Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix with Ollama:
ollama run hf.co/Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M
- Unsloth Studio
How to use Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix 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 Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix 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 Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix to start chatting
- Docker Model Runner
How to use Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix with Docker Model Runner:
docker model run hf.co/Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M
- Lemonade
How to use Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lewdiculous/Kool-Aid_7B-GGUF-IQ-Imatrix:Q4_K_M
Run and chat with the model
lemonade run user.Kool-Aid_7B-GGUF-IQ-Imatrix-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Create README.md
Browse files
README.md
ADDED
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---
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library_name: transformers
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tags:
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- mergekit
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- merge
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- roleplay
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- mistral
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license: other
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---
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This repository hosts GGUF-IQ-Imatrix quants for [ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b](https://huggingface.co/ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b).
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**What does "Imatrix" mean?**
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It stands for **Importance Matrix**, a technique used to improve the quality of quantized models.
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The **Imatrix** is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process.
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The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse.
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[[1]](https://github.com/ggerganov/llama.cpp/discussions/5006) [[2]](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
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For imatrix data generation, kalomaze's `groups_merged.txt` with added roleplay chats was used, you can find it [here](https://huggingface.co/Lewdiculous/Datura_7B-GGUF-Imatrix/blob/main/imatrix-with-rp-format-data.txt). This was just to add a bit more diversity to the data.
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**Steps:**
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```
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Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
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```
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*Using the latest llama.cpp at the time.*
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```python
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quantization_options = [
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"Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S",
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"Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
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]
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```
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---
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This model was merged using the SLERP merge method.
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### Models Merged
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The following models were included in the merge:
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* [Nitral-AI/Prima-LelantaclesV6.69-7b](https://huggingface.co/Nitral-AI/Prima-LelantaclesV6.69-7b)
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* [Nitral-AI/Prima-LelantaclesV6.31-7b](https://huggingface.co/Nitral-AI/Prima-LelantaclesV6.31-7b)
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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slices:
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- sources:
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- model: Nitral-AI/Prima-LelantaclesV6.69-7b
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layer_range: [0, 32]
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- model: Nitral-AI/Prima-LelantaclesV6.31-7b
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layer_range: [0, 32]
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merge_method: slerp
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base_model: Nitral-AI/Prima-LelantaclesV6.69-7b
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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dtype: bfloat16
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```
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