Instructions to use matrixportalx/aya-23-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matrixportalx/aya-23-8B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("matrixportalx/aya-23-8B-GGUF", dtype="auto") - llama-cpp-python
How to use matrixportalx/aya-23-8B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="matrixportalx/aya-23-8B-GGUF", filename="aya-23-8b-f16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use matrixportalx/aya-23-8B-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 matrixportalx/aya-23-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf matrixportalx/aya-23-8B-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 matrixportalx/aya-23-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf matrixportalx/aya-23-8B-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 matrixportalx/aya-23-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf matrixportalx/aya-23-8B-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 matrixportalx/aya-23-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf matrixportalx/aya-23-8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/matrixportalx/aya-23-8B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use matrixportalx/aya-23-8B-GGUF with Ollama:
ollama run hf.co/matrixportalx/aya-23-8B-GGUF:Q4_K_M
- Unsloth Studio
How to use matrixportalx/aya-23-8B-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 matrixportalx/aya-23-8B-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 matrixportalx/aya-23-8B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for matrixportalx/aya-23-8B-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use matrixportalx/aya-23-8B-GGUF with Docker Model Runner:
docker model run hf.co/matrixportalx/aya-23-8B-GGUF:Q4_K_M
- Lemonade
How to use matrixportalx/aya-23-8B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull matrixportalx/aya-23-8B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.aya-23-8B-GGUF-Q4_K_M
List all available models
lemonade list
File size: 8,228 Bytes
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base_model: CohereForAI/aya-23-8B
language:
- en
- fr
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- pt
- ja
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- zh
- ar
- el
- fa
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library_name: transformers
license: cc-by-nc-4.0
tags:
- llama-cpp
- matrixportal
inference: false
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I agree to use this model for non-commercial use ONLY: checkbox
---
# matrixportal/aya-23-8B-GGUF
This model was converted to GGUF format from [`CohereForAI/aya-23-8B`](https://huggingface.co/CohereForAI/aya-23-8B) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
Refer to the [original model card](https://huggingface.co/CohereForAI/aya-23-8B) for more details on the model.
## ✅ Quantized Models Download List
**✨ Recommended for CPU:** `Q4_K_M` | **⚡ Recommended for ARM CPU:** `Q4_0` | **🏆 Best Quality:** `Q8_0`
| 🚀 Download | 🔢 Type | 📝 Notes |
|:---------|:-----|:------|
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q2_k.gguf) |  | Basic quantization |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q3_k_s.gguf) |  | Small size |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q3_k_m.gguf) |  | Balanced quality |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q3_k_l.gguf) |  | Better quality |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q4_0.gguf) |  | Fast on ARM |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q4_k_s.gguf) |  | Fast, recommended |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q4_k_m.gguf) |  ⭐ | Best balance |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q5_0.gguf) |  | Good quality |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q5_k_s.gguf) |  | Balanced |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q5_k_m.gguf) |  | High quality |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q6_k.gguf) |  🏆 | Very good quality |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-q8_0.gguf) |  ⚡ | Fast, best quality |
| [Download](https://huggingface.co/matrixportal/aya-23-8B-GGUF/resolve/main/aya-23-8b-f16.gguf) |  | Maximum accuracy |
💡 **Tip:** Use `F16` for maximum precision when quality is critical
|