Instructions to use llmware/slim-qa-gen-phi-3-tool with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmware/slim-qa-gen-phi-3-tool with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("llmware/slim-qa-gen-phi-3-tool", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use llmware/slim-qa-gen-phi-3-tool 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 llmware/slim-qa-gen-phi-3-tool # Run inference directly in the terminal: llama cli -hf llmware/slim-qa-gen-phi-3-tool
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf llmware/slim-qa-gen-phi-3-tool # Run inference directly in the terminal: llama cli -hf llmware/slim-qa-gen-phi-3-tool
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 llmware/slim-qa-gen-phi-3-tool # Run inference directly in the terminal: ./llama-cli -hf llmware/slim-qa-gen-phi-3-tool
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 llmware/slim-qa-gen-phi-3-tool # Run inference directly in the terminal: ./build/bin/llama-cli -hf llmware/slim-qa-gen-phi-3-tool
Use Docker
docker model run hf.co/llmware/slim-qa-gen-phi-3-tool
- LM Studio
- Jan
- Ollama
How to use llmware/slim-qa-gen-phi-3-tool with Ollama:
ollama run hf.co/llmware/slim-qa-gen-phi-3-tool
- Unsloth Desktop
- Docker Model Runner
How to use llmware/slim-qa-gen-phi-3-tool with Docker Model Runner:
docker model run hf.co/llmware/slim-qa-gen-phi-3-tool
- Lemonade
How to use llmware/slim-qa-gen-phi-3-tool with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull llmware/slim-qa-gen-phi-3-tool
Run and chat with the model
lemonade run user.slim-qa-gen-phi-3-tool-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| license: apache-2.0 | |
| # SLIM-QA-GEN-PHI-3-TOOL | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| **slim-qa-gen-phi-3-tool** is a 4_K_M quantized GGUF version of slim-qa-gen-phi-3, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. | |
| This model implements a generative 'question' and 'answer' (e.g., 'qa-gen') function, which takes a context passage as an input, and then generates as an output a python dictionary consisting of two keys: | |
| `{'question': ['What was the amount of revenue in the quarter?'], 'answer': ['$3.2 billion']} ` | |
| The model has been designed to accept one of three different parameters to guide the type of question-answer created: | |
| -- 'question, answer' (generates a standard question and answer), | |
| -- 'boolean' (generates a 'yes-no' question and answer), and | |
| -- 'multiple choice' (generates a multiple choice question and answer). | |
| Note: we would generally recommend using sampling and temperature(0.5+) for varied generations, although if using 'multiple choice' mode, then we have seen the best results with temperature in the 0.2-0.3 range. | |
| [**slim-qa-gen-phi-3**](https://huggingface.co/llmware/slim-qa-gen-phi-3) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation. | |
| To pull the model via API: | |
| from huggingface_hub import snapshot_download | |
| snapshot_download("llmware/slim-qa-gen-phi-3-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) | |
| Load in your favorite GGUF inference engine, or try with llmware as follows: | |
| from llmware.models import ModelCatalog | |
| # to load the model and make a basic inference | |
| model = ModelCatalog().load_model("slim-qa-gen-phi-3-tool", temperature=0.5, sample=True) | |
| response = model.function_call(text_sample, params=["boolean"]) | |
| # this one line will download the model and run a series of tests | |
| ModelCatalog().tool_test_run("slim-qa-gen-phi-3-tool", verbose=True) | |
| Note: please review [**config.json**](https://huggingface.co/llmware/slim-qa-gen-phi-3-tool/blob/main/config.json) in the repository for prompt template information, details on the model, and full test set. | |
| ## Model Card Contact | |
| Darren Oberst & llmware team | |
| [Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h) |