Instructions to use madhuHuggingface/functiongemma-ec2-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madhuHuggingface/functiongemma-ec2-finetuned with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("madhuHuggingface/functiongemma-ec2-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use madhuHuggingface/functiongemma-ec2-finetuned 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 madhuHuggingface/functiongemma-ec2-finetuned:Q8_0 # Run inference directly in the terminal: llama cli -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0 # Run inference directly in the terminal: llama cli -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
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 madhuHuggingface/functiongemma-ec2-finetuned:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
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 madhuHuggingface/functiongemma-ec2-finetuned:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
Use Docker
docker model run hf.co/madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
- LM Studio
- Jan
- Ollama
How to use madhuHuggingface/functiongemma-ec2-finetuned with Ollama:
ollama run hf.co/madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
- Unsloth Studio
How to use madhuHuggingface/functiongemma-ec2-finetuned 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 madhuHuggingface/functiongemma-ec2-finetuned 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 madhuHuggingface/functiongemma-ec2-finetuned to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for madhuHuggingface/functiongemma-ec2-finetuned to start chatting
- Pi
How to use madhuHuggingface/functiongemma-ec2-finetuned with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "madhuHuggingface/functiongemma-ec2-finetuned:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use madhuHuggingface/functiongemma-ec2-finetuned with Docker Model Runner:
docker model run hf.co/madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
- Lemonade
How to use madhuHuggingface/functiongemma-ec2-finetuned with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
Run and chat with the model
lemonade run user.functiongemma-ec2-finetuned-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use madhuHuggingface/functiongemma-ec2-finetuned with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use madhuHuggingface/functiongemma-ec2-finetuned with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "madhuHuggingface/functiongemma-ec2-finetuned:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Training in progress, step 4700
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| 3364 |
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| 3365 |
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| 3366 |
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| 3367 |
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| 3368 |
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| 3369 |
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| 3370 |
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| 3371 |
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| 3372 |
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| 3373 |
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|
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|
| 3382 |
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|
| 3383 |
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|
| 3384 |
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| 3385 |
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"should_training_stop": true
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| 3386 |
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| 3387 |
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| 3388 |
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| 3389 |
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| 3390 |
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| 3391 |
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| 3392 |
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last-checkpoint/training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
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|
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:
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| 3 |
size 6353
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|
| 1 |
version https://git-lfs.github.com/spec/v1
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size 6353
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