Instructions to use RichardErkhov/dacorvo_-_Mixtral-tiny-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use RichardErkhov/dacorvo_-_Mixtral-tiny-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RichardErkhov/dacorvo_-_Mixtral-tiny-gguf", filename="Mixtral-tiny.IQ3_M.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 RichardErkhov/dacorvo_-_Mixtral-tiny-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 RichardErkhov/dacorvo_-_Mixtral-tiny-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/dacorvo_-_Mixtral-tiny-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 RichardErkhov/dacorvo_-_Mixtral-tiny-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/dacorvo_-_Mixtral-tiny-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 RichardErkhov/dacorvo_-_Mixtral-tiny-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/dacorvo_-_Mixtral-tiny-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 RichardErkhov/dacorvo_-_Mixtral-tiny-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/dacorvo_-_Mixtral-tiny-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/dacorvo_-_Mixtral-tiny-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/dacorvo_-_Mixtral-tiny-gguf with Ollama:
ollama run hf.co/RichardErkhov/dacorvo_-_Mixtral-tiny-gguf:Q4_K_M
- Unsloth Studio
How to use RichardErkhov/dacorvo_-_Mixtral-tiny-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 RichardErkhov/dacorvo_-_Mixtral-tiny-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 RichardErkhov/dacorvo_-_Mixtral-tiny-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RichardErkhov/dacorvo_-_Mixtral-tiny-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use RichardErkhov/dacorvo_-_Mixtral-tiny-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/dacorvo_-_Mixtral-tiny-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/dacorvo_-_Mixtral-tiny-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/dacorvo_-_Mixtral-tiny-gguf:Q4_K_M
Run and chat with the model
lemonade run user.dacorvo_-_Mixtral-tiny-gguf-Q4_K_M
List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
Mixtral-tiny - GGUF
- Model creator: https://huggingface.co/dacorvo/
- Original model: https://huggingface.co/dacorvo/Mixtral-tiny/
| Name | Quant method | Size |
|---|---|---|
| Mixtral-tiny.Q2_K.gguf | Q2_K | 0.1GB |
| Mixtral-tiny.IQ3_XS.gguf | IQ3_XS | 0.11GB |
| Mixtral-tiny.IQ3_S.gguf | IQ3_S | 0.11GB |
| Mixtral-tiny.Q3_K_S.gguf | Q3_K_S | 0.11GB |
| Mixtral-tiny.IQ3_M.gguf | IQ3_M | 0.12GB |
| Mixtral-tiny.Q3_K.gguf | Q3_K | 0.12GB |
| Mixtral-tiny.Q3_K_M.gguf | Q3_K_M | 0.12GB |
| Mixtral-tiny.Q3_K_L.gguf | Q3_K_L | 0.13GB |
| Mixtral-tiny.IQ4_XS.gguf | IQ4_XS | 0.13GB |
| Mixtral-tiny.Q4_0.gguf | Q4_0 | 0.14GB |
| Mixtral-tiny.IQ4_NL.gguf | IQ4_NL | 0.14GB |
| Mixtral-tiny.Q4_K_S.gguf | Q4_K_S | 0.14GB |
| Mixtral-tiny.Q4_K.gguf | Q4_K | 0.15GB |
| Mixtral-tiny.Q4_K_M.gguf | Q4_K_M | 0.15GB |
| Mixtral-tiny.Q4_1.gguf | Q4_1 | 0.15GB |
| Mixtral-tiny.Q5_0.gguf | Q5_0 | 0.16GB |
| Mixtral-tiny.Q5_K_S.gguf | Q5_K_S | 0.16GB |
| Mixtral-tiny.Q5_K.gguf | Q5_K | 0.17GB |
| Mixtral-tiny.Q5_K_M.gguf | Q5_K_M | 0.17GB |
| Mixtral-tiny.Q5_1.gguf | Q5_1 | 0.18GB |
| Mixtral-tiny.Q6_K.gguf | Q6_K | 0.19GB |
| Mixtral-tiny.Q8_0.gguf | Q8_0 | 0.25GB |
Original model description: Entry not found
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