Instructions to use gnokit/gemma_2b_coedit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gnokit/gemma_2b_coedit with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("gnokit/gemma_2b_coedit", dtype="auto") - llama-cpp-python
How to use gnokit/gemma_2b_coedit with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="gnokit/gemma_2b_coedit", filename="gemma_2b_coedit-unsloth.F16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use gnokit/gemma_2b_coedit with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf gnokit/gemma_2b_coedit:Q4_K_M # Run inference directly in the terminal: llama-cli -hf gnokit/gemma_2b_coedit:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf gnokit/gemma_2b_coedit:Q4_K_M # Run inference directly in the terminal: llama-cli -hf gnokit/gemma_2b_coedit: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 gnokit/gemma_2b_coedit:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf gnokit/gemma_2b_coedit: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 gnokit/gemma_2b_coedit:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf gnokit/gemma_2b_coedit:Q4_K_M
Use Docker
docker model run hf.co/gnokit/gemma_2b_coedit:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use gnokit/gemma_2b_coedit with Ollama:
ollama run hf.co/gnokit/gemma_2b_coedit:Q4_K_M
- Unsloth Studio new
How to use gnokit/gemma_2b_coedit 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 gnokit/gemma_2b_coedit 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 gnokit/gemma_2b_coedit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for gnokit/gemma_2b_coedit to start chatting
- Docker Model Runner
How to use gnokit/gemma_2b_coedit with Docker Model Runner:
docker model run hf.co/gnokit/gemma_2b_coedit:Q4_K_M
- Lemonade
How to use gnokit/gemma_2b_coedit with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gnokit/gemma_2b_coedit:Q4_K_M
Run and chat with the model
lemonade run user.gemma_2b_coedit-Q4_K_M
List all available models
lemonade list
Model Details:
- This model was created by finetuning the unsloth/gemma-1.1-2b-it-bnb-4bit model using the coedit dataset from Grammarly.
- The finetuning was done following the fine-tuning notebook provided by Unsloth as a practice of finetuning using the coedit dataset.
- The model was finetuned using the prompt format of the gemma-2b-it model.
<start_of_turn>user
Fix grammar in this sentence: A notable number of Chinese factories make piratical products by copying foreign products.<end_of_turn>
<start_of_turn>model
A notable number of Chinese factories make pirated products by copying foreign products.<end_of_turn>
- The finetuning was done 2x faster by utilizing the Unsloth and Hugging Face's TRL library.
Limitations:
The model was finetuned on a specific dataset (coedit) and may not generalize well to all Italian text generation tasks. Its performance may be limited compared to models trained on larger and more diverse datasets.
Uploaded model
- Developed by: gnokit
- License: apache-2.0
- Finetuned from model : unsloth/gemma-1.1-2b-it-bnb-4bit
This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.
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Model tree for gnokit/gemma_2b_coedit
Base model
unsloth/gemma-1.1-2b-it-bnb-4bit