Instructions to use hongyongjiang/python-reference-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use hongyongjiang/python-reference-models 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 hongyongjiang/python-reference-models:F16
# Run inference directly in the terminal:
llama cli -hf hongyongjiang/python-reference-models:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hongyongjiang/python-reference-models:F16
# Run inference directly in the terminal:
llama cli -hf hongyongjiang/python-reference-models: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 hongyongjiang/python-reference-models:F16
# Run inference directly in the terminal:
./llama-cli -hf hongyongjiang/python-reference-models: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 hongyongjiang/python-reference-models:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hongyongjiang/python-reference-models:F16

Use Docker

docker model run hf.co/hongyongjiang/python-reference-models:F16

LM Studio
Jan
Ollama
How to use hongyongjiang/python-reference-models with Ollama:
```
ollama run hf.co/hongyongjiang/python-reference-models:F16
```

Unsloth Studio

How to use hongyongjiang/python-reference-models 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 hongyongjiang/python-reference-models 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 hongyongjiang/python-reference-models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hongyongjiang/python-reference-models to start chatting

Docker Model Runner
How to use hongyongjiang/python-reference-models with Docker Model Runner:
```
docker model run hf.co/hongyongjiang/python-reference-models:F16
```

Lemonade

How to use hongyongjiang/python-reference-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hongyongjiang/python-reference-models:F16

Run and chat with the model

lemonade run user.python-reference-models-F16

List all available models

lemonade list

Atomic Chat

python-reference-models / llama3.2-1b_bin /layer_4.bin

hongyongjiang

Upload folder using huggingface_hub

30f74c8 verified 5 months ago

Download

History Blame Contribute Delete

61 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: a553495a54e5e05686a5f7fafcc13a67848eeb532a047450a878cb3ff5a4f841
Size of remote file:: 61 MB
SHA256:: 0ecef27613830f7285dab23653620752ad8365fa413735320c701fd5f8205d6f

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

hongyongjiang
/

python-reference-models

Pickle imports

Xet Pointer Details