Instructions to use Kquant03/PygWin-2x7B-FP16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Kquant03/PygWin-2x7B-FP16-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Kquant03/PygWin-2x7B-FP16-GGUF", filename="ggml-model-f16.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 Kquant03/PygWin-2x7B-FP16-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 Kquant03/PygWin-2x7B-FP16-GGUF:F16 # Run inference directly in the terminal: llama cli -hf Kquant03/PygWin-2x7B-FP16-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Kquant03/PygWin-2x7B-FP16-GGUF:F16 # Run inference directly in the terminal: llama cli -hf Kquant03/PygWin-2x7B-FP16-GGUF:F16
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 Kquant03/PygWin-2x7B-FP16-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf Kquant03/PygWin-2x7B-FP16-GGUF:F16
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 Kquant03/PygWin-2x7B-FP16-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Kquant03/PygWin-2x7B-FP16-GGUF:F16
Use Docker
docker model run hf.co/Kquant03/PygWin-2x7B-FP16-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use Kquant03/PygWin-2x7B-FP16-GGUF with Ollama:
ollama run hf.co/Kquant03/PygWin-2x7B-FP16-GGUF:F16
- Unsloth Studio
How to use Kquant03/PygWin-2x7B-FP16-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 Kquant03/PygWin-2x7B-FP16-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 Kquant03/PygWin-2x7B-FP16-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Kquant03/PygWin-2x7B-FP16-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Kquant03/PygWin-2x7B-FP16-GGUF with Docker Model Runner:
docker model run hf.co/Kquant03/PygWin-2x7B-FP16-GGUF:F16
- Lemonade
How to use Kquant03/PygWin-2x7B-FP16-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Kquant03/PygWin-2x7B-FP16-GGUF:F16
Run and chat with the model
lemonade run user.PygWin-2x7B-FP16-GGUF-F16
List all available models
lemonade list
About quants
Have you tried quantizing down to the older Q4_0, Q4_1, q5_0s? i recall mixtral 8x7b models having problems with the newer quant methods but working fine with the older types.
It's mentioned in the beginning of this article
https://rentry.org/HowtoMixtral
And Undi mentions it in this model card
https://huggingface.co/Undi95/Toppy-Mix-4x7B-GGUF
Have you tried quantizing down to the older Q4_0, Q4_1, q5_0s? i recall mixtral 8x7b models having problems with the newer quant methods but working fine with the older types.
It's mentioned in the beginning of this article
https://rentry.org/HowtoMixtral
And Undi mentions it in this model card
https://huggingface.co/Undi95/Toppy-Mix-4x7B-GGUF
no I didn't try this, does q4_0 still exist on the new versions of llama.cpp?
I believe it still supports it, i can still make Q8_0 quants successfully. Although i haven't updated it since late january.
It lists all the Q4_0 - Q8_1, Q2_k - Q8_k and IQ quants in here, so i'd guess they're still in the most recent releases?
https://github.com/ggerganov/llama.cpp/blob/master/ggml-quants.h
It lists all the Q4_0 - Q8_1, Q2_k - Q8_k and IQ quants in here, so i'd guess they're still in the most recent releases?
https://github.com/ggerganov/llama.cpp/blob/master/ggml-quants.h
alright I just tried it and it didn't work, this was a great idea though :)
I tried to quant it too, it's cursed. Crashes kobold instantly. Doesn't even give an error
I tried to quant it too, it's cursed. Crashes kobold instantly. Doesn't even give an error
The Cursed Pygmalion-Xwin Mixture