Sentence Similarity
sentence-transformers
GGUF
Transformers
mteb
Qwen2
TensorBlock
GGUF
Eval Results (legacy)
conversational
Instructions to use tensorblock/gte-Qwen2-7B-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tensorblock/gte-Qwen2-7B-instruct-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tensorblock/gte-Qwen2-7B-instruct-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 tensorblock/gte-Qwen2-7B-instruct-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tensorblock/gte-Qwen2-7B-instruct-GGUF", dtype="auto") - llama-cpp-python
How to use tensorblock/gte-Qwen2-7B-instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tensorblock/gte-Qwen2-7B-instruct-GGUF", filename="gte-Qwen2-7B-instruct-Q2_K.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 Settings
- llama.cpp
How to use tensorblock/gte-Qwen2-7B-instruct-GGUF 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 tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K
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 tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K
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 tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use tensorblock/gte-Qwen2-7B-instruct-GGUF with Ollama:
ollama run hf.co/tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K
- Unsloth Studio
How to use tensorblock/gte-Qwen2-7B-instruct-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 tensorblock/gte-Qwen2-7B-instruct-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 tensorblock/gte-Qwen2-7B-instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tensorblock/gte-Qwen2-7B-instruct-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tensorblock/gte-Qwen2-7B-instruct-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K
- Lemonade
How to use tensorblock/gte-Qwen2-7B-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/gte-Qwen2-7B-instruct-GGUF:Q2_K
Run and chat with the model
lemonade run user.gte-Qwen2-7B-instruct-GGUF-Q2_K
List all available models
lemonade list
- Xet hash:
- c1b28e3c49fcb535a763d4299fb3138f36b518f6454a0e1a5ae702ff6d3eaefe
- Size of remote file:
- 3.01 GB
- SHA256:
- dbbe4d228829e9d429b9c3f5301a6a340cb1ef7983b1d8d18657ac2be7f7218c
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