Instructions to use QuantFactory/Triplex-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/Triplex-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Triplex-GGUF", filename="Triplex.Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/Triplex-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Triplex-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Triplex-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Triplex-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Triplex-GGUF: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 QuantFactory/Triplex-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Triplex-GGUF: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 QuantFactory/Triplex-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Triplex-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Triplex-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/Triplex-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/Triplex-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/Triplex-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/Triplex-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/Triplex-GGUF with Ollama:
ollama run hf.co/QuantFactory/Triplex-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/Triplex-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 QuantFactory/Triplex-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 QuantFactory/Triplex-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/Triplex-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/Triplex-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Triplex-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Triplex-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Triplex-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Triplex-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/Triplex-GGUF
This is quantized version of SciPhi/Triplex created using llama.cpp
Original Model Card
Triplex: a SOTA LLM for knowledge graph construction.
Knowledge graphs, like Microsoft's Graph RAG, enhance RAG methods but are expensive to build. Triplex offers a 98% cost reduction for knowledge graph creation, outperforming GPT-4 at 1/60th the cost and enabling local graph building with SciPhi's R2R.
Triplex is a finetuned version of Phi3-3.8B for creating knowledge graphs from unstructured data developed by SciPhi.AI. It works by extracting triplets - simple statements consisting of a subject, predicate, and object - from text or other data sources.
Benchmark
Usage:
- Blog: https://www.sciphi.ai/blog/triplex
- Demo: kg.sciphi.ai
- Cookbook: https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph
- Python:
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
def triplextract(model, tokenizer, text, entity_types, predicates):
input_format = """
**Entity Types:**
{entity_types}
**Predicates:**
{predicates}
**Text:**
{text}
"""
message = input_format.format(
entity_types = json.dumps({"entity_types": entity_types}),
predicates = json.dumps({"predicates": predicates}),
text = text)
messages = [{'role': 'user', 'content': message}]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt = True, return_tensors="pt").to("cuda")
output = tokenizer.decode(model.generate(input_ids=input_ids, max_length=2048)[0], skip_special_tokens=True)
return output
model = AutoModelForCausalLM.from_pretrained("sciphi/triplex", trust_remote_code=True).to('cuda').eval()
tokenizer = AutoTokenizer.from_pretrained("sciphi/triplex", trust_remote_code=True)
entity_types = [ "LOCATION", "POSITION", "DATE", "CITY", "COUNTRY", "NUMBER" ]
predicates = [ "POPULATION", "AREA" ]
text = """
San Francisco,[24] officially the City and County of San Francisco, is a commercial, financial, and cultural center in Northern California.
With a population of 808,437 residents as of 2022, San Francisco is the fourth most populous city in the U.S. state of California behind Los Angeles, San Diego, and San Jose.
"""
prediction = triplextract(model, tokenizer, text, entity_types, predicates)
print(prediction)
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