Instructions to use agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF", dtype="auto") - llama-cpp-python
How to use agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF", filename="gte-qwen1.5-7b-instruct-q5_k_m.gguf", )
llm.create_chat_completion( messages = "{\n \"source_sentence\": \"That is a happy person\",\n \"sentences\": [\n \"That is a happy dog\",\n \"That is a very happy person\",\n \"Today is a sunny day\"\n ]\n}" ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama-cli -hf agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: llama-cli -hf agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_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 agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_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 agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M
Use Docker
docker model run hf.co/agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M
- LM Studio
- Jan
- Ollama
How to use agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF with Ollama:
ollama run hf.co/agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M
- Unsloth Studio new
How to use agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-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 agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-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 agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF to start chatting
- Docker Model Runner
How to use agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF with Docker Model Runner:
docker model run hf.co/agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M
- Lemonade
How to use agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF:Q5_K_M
Run and chat with the model
lemonade run user.gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF-Q5_K_M
List all available models
lemonade list
agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF
This model was converted to GGUF format from Alibaba-NLP/gte-Qwen1.5-7B-instruct using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew.
brew install ggerganov/ggerganov/llama.cpp
Invoke the llama.cpp server or the CLI. CLI:
llama-cli --hf-repo agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF --model gte-qwen1.5-7b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF --model gte-qwen1.5-7b-instruct-q5_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
git clone https://github.com/ggerganov/llama.cpp && \
cd llama.cpp && \
make && \
./main -m gte-qwen1.5-7b-instruct-q5_k_m.gguf -n 128
- Downloads last month
- 8
5-bit
Spaces using agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF 11
Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported83.164
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported49.377
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported77.530
- accuracy on MTEB AmazonPolarityClassificationtest set self-reported96.696
- ap on MTEB AmazonPolarityClassificationtest set self-reported94.909
- f1 on MTEB AmazonPolarityClassificationtest set self-reported96.695
- accuracy on MTEB AmazonReviewsClassification (en)test set self-reported62.168
- f1 on MTEB AmazonReviewsClassification (en)test set self-reported60.411