Instructions to use hongyongjiang/python-reference-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use hongyongjiang/python-reference-models with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="hongyongjiang/python-reference-models", filename="gemma-3-4b-it_bin/gemma-3-4b-it-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 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
- Atomic Chat new
- 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
Upload folder using huggingface_hub
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oid sha256:8f780c038da0b7e121d62f0e2911535b9a6984bfadced2023eb6eafa71fbadc6
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| 3 |
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size 5120
|
qwen2.5-vl-3b_bias_bin/layer_8.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:0347b8ed0b3f5643b8f87735035f591e360e3cf063e7c8de87f482f2c99b0ab3
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| 3 |
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size 5120
|
qwen2.5-vl-3b_bias_bin/layer_9.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:cb1d6addf1848cdcfecabe34d89728f579bb3f98186087ea74410ff80c7b1a00
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| 3 |
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size 5120
|