Instructions to use second-state/gte-Qwen2-1.5B-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/gte-Qwen2-1.5B-instruct-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("second-state/gte-Qwen2-1.5B-instruct-GGUF", trust_remote_code=True)

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 second-state/gte-Qwen2-1.5B-instruct-GGUF with Transformers:

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("second-state/gte-Qwen2-1.5B-instruct-GGUF", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("second-state/gte-Qwen2-1.5B-instruct-GGUF", trust_remote_code=True)

llama-cpp-python

How to use second-state/gte-Qwen2-1.5B-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/gte-Qwen2-1.5B-instruct-GGUF",
	filename="gte-Qwen2-1.5B-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

llama.cpp

How to use second-state/gte-Qwen2-1.5B-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/gte-Qwen2-1.5B-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/gte-Qwen2-1.5B-instruct-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 second-state/gte-Qwen2-1.5B-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/gte-Qwen2-1.5B-instruct-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 second-state/gte-Qwen2-1.5B-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/gte-Qwen2-1.5B-instruct-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 second-state/gte-Qwen2-1.5B-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/gte-Qwen2-1.5B-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/gte-Qwen2-1.5B-instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use second-state/gte-Qwen2-1.5B-instruct-GGUF with Ollama:
```
ollama run hf.co/second-state/gte-Qwen2-1.5B-instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use second-state/gte-Qwen2-1.5B-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 second-state/gte-Qwen2-1.5B-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 second-state/gte-Qwen2-1.5B-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 second-state/gte-Qwen2-1.5B-instruct-GGUF to start chatting

Docker Model Runner
How to use second-state/gte-Qwen2-1.5B-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/gte-Qwen2-1.5B-instruct-GGUF:Q4_K_M
```

Lemonade

How to use second-state/gte-Qwen2-1.5B-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/gte-Qwen2-1.5B-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gte-Qwen2-1.5B-instruct-GGUF-Q4_K_M

List all available models

lemonade list

gte-Qwen2-1.5B-instruct-GGUF

Original Model

Alibaba-NLP/gte-Qwen2-1.5B-instruct

Run with LlamaEdge

LlamaEdge version: v0.12.2 and above
Prompt template
- Prompt type: embedding
Context size: 32000

Run as LlamaEdge service

wasmedge --dir .:. --nn-preload default:GGML:AUTO:gte-Qwen2-1.5B-instruct-Q5_K_M.gguf \
  llama-api-server.wasm \
  --prompt-template embedding \
  --ctx-size 32000 \
  --model-name gte-Qwen2-1.5B-instruct

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
gte-Qwen2-1.5B-instruct-Q2_K.gguf	Q2_K	2	752 MB	smallest, significant quality loss - not recommended for most purposes
gte-Qwen2-1.5B-instruct-Q3_K_L.gguf	Q3_K_L	3	980 MB	small, substantial quality loss
gte-Qwen2-1.5B-instruct-Q3_K_M.gguf	Q3_K_M	3	924 MB	very small, high quality loss
gte-Qwen2-1.5B-instruct-Q3_K_S.gguf	Q3_K_S	3	861 MB	very small, high quality loss
gte-Qwen2-1.5B-instruct-Q4_0.gguf	Q4_0	4	1.07 GB	legacy; small, very high quality loss - prefer using Q3_K_M
gte-Qwen2-1.5B-instruct-Q4_K_M.gguf	Q4_K_M	4	1.12 GB	medium, balanced quality - recommended
gte-Qwen2-1.5B-instruct-Q4_K_S.gguf	Q4_K_S	4	1.07 GB	small, greater quality loss
gte-Qwen2-1.5B-instruct-Q5_0.gguf	Q5_0	5	1.26 GB	legacy; medium, balanced quality - prefer using Q4_K_M
gte-Qwen2-1.5B-instruct-Q5_K_M.gguf	Q5_K_M	5	1.28 GB	large, very low quality loss - recommended
gte-Qwen2-1.5B-instruct-Q5_K_S.gguf	Q5_K_S	5	1.26 GB	large, low quality loss - recommended
gte-Qwen2-1.5B-instruct-Q6_K.gguf	Q6_K	6	1.46 GB	very large, extremely low quality loss
gte-Qwen2-1.5B-instruct-Q8_0.gguf	Q8_0	8	1.89 GB	very large, extremely low quality loss - not recommended
gte-Qwen2-1.5B-instruct-f16.gguf	f16	8	3.56 GB	very large, extremely low quality loss - not recommended

Quantized with llama.cpp b3259

Downloads last month: 1,006

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for second-state/gte-Qwen2-1.5B-instruct-GGUF

Base model

Alibaba-NLP/gte-Qwen2-1.5B-instruct

Quantized

(23)

this model

second-state
/

gte-Qwen2-1.5B-instruct-GGUF

gte-Qwen2-1.5B-instruct-GGUF

Original Model

Run with LlamaEdge

Quantized GGUF Models

Model tree for second-state/gte-Qwen2-1.5B-instruct-GGUF

Spaces using second-state/gte-Qwen2-1.5B-instruct-GGUF 11