Instructions to use robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("robert-lo/gte-Qwen2-7B-instruct-Q4_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 robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF",
	filename="gte-qwen2-7b-instruct-q4_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 Settings

llama.cpp

How to use robert-lo/gte-Qwen2-7B-instruct-Q4_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 robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-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 robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-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 robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-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 robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use robert-lo/gte-Qwen2-7B-instruct-Q4_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 robert-lo/gte-Qwen2-7B-instruct-Q4_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 robert-lo/gte-Qwen2-7B-instruct-Q4_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 robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gte-Qwen2-7B-instruct-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF

This model was converted to GGUF format from Alibaba-NLP/gte-Qwen2-7B-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 (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_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.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -c 2048

Downloads last month: 3

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for robert-lo/gte-Qwen2-7B-instruct-Q4_K_M-GGUF

Base model

Alibaba-NLP/gte-Qwen2-7B-instruct

Quantized

(36)

this model

Evaluation results

accuracy on MTEB AmazonCounterfactualClassification (en)
test set self-reported

91.313
ap on MTEB AmazonCounterfactualClassification (en)
test set self-reported

67.643
f1 on MTEB AmazonCounterfactualClassification (en)
test set self-reported

87.534
accuracy on MTEB AmazonPolarityClassification
test set self-reported

97.498
ap on MTEB AmazonPolarityClassification
test set self-reported

96.303
f1 on MTEB AmazonPolarityClassification
test set self-reported

97.498
accuracy on MTEB AmazonReviewsClassification (en)
test set self-reported

62.564
f1 on MTEB AmazonReviewsClassification (en)
test set self-reported

60.976