Instructions to use llmware/slim-xsum-phi-3-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmware/slim-xsum-phi-3-gguf with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("llmware/slim-xsum-phi-3-gguf", dtype="auto") - llama-cpp-python
How to use llmware/slim-xsum-phi-3-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="llmware/slim-xsum-phi-3-gguf", filename="slim-xsum.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 llmware/slim-xsum-phi-3-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf llmware/slim-xsum-phi-3-gguf # Run inference directly in the terminal: llama-cli -hf llmware/slim-xsum-phi-3-gguf
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf llmware/slim-xsum-phi-3-gguf # Run inference directly in the terminal: llama-cli -hf llmware/slim-xsum-phi-3-gguf
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-xsum-phi-3-gguf # Run inference directly in the terminal: ./llama-cli -hf llmware/slim-xsum-phi-3-gguf
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-xsum-phi-3-gguf # Run inference directly in the terminal: ./build/bin/llama-cli -hf llmware/slim-xsum-phi-3-gguf
Use Docker
docker model run hf.co/llmware/slim-xsum-phi-3-gguf
- LM Studio
- Jan
- Ollama
How to use llmware/slim-xsum-phi-3-gguf with Ollama:
ollama run hf.co/llmware/slim-xsum-phi-3-gguf
- Unsloth Studio new
How to use llmware/slim-xsum-phi-3-gguf 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 llmware/slim-xsum-phi-3-gguf 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 llmware/slim-xsum-phi-3-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for llmware/slim-xsum-phi-3-gguf to start chatting
- Docker Model Runner
How to use llmware/slim-xsum-phi-3-gguf with Docker Model Runner:
docker model run hf.co/llmware/slim-xsum-phi-3-gguf
- Lemonade
How to use llmware/slim-xsum-phi-3-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull llmware/slim-xsum-phi-3-gguf
Run and chat with the model
lemonade run user.slim-xsum-phi-3-gguf-{{QUANT_TAG}}List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)SLIM-XSUM-PHI-3-GGUF
slim-xsum-phi-3-gguf is a fine-tune of Phi-3 that implements an 'extreme summarization' (e.g., 'xsum') function call based on the parameter key "xsum" that generates an LLM text output in the form of a python dictionary as follows:
`{'xsum': ['Stock Market declines on worries of interest rates.']} `
The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs through the use of function-calling and small specialized LLMs.
slim-xsum 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-xsum-phi-3-gguf", 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-xsum-phi-3-gguf")
response = model.function_call(text_sample)
# this one line will download the model and run a series of tests
ModelCatalog().tool_test_run("slim-xsum-phi-3-gguf", verbose=True)
Note: please review config.json in the repository for prompt wrapping information, details on the model, and full test set.
Model Card Contact
Darren Oberst & llmware team
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We're not able to determine the quantization variants.
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="llmware/slim-xsum-phi-3-gguf", filename="slim-xsum.gguf", )