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 Settings

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

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 / config.json

apepkuss79

Upload config.json with huggingface_hub

afb83dd verified almost 2 years ago

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	{
	"architectures": [
	"Qwen2ForCausalLM"
	],
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoModel": "modeling_qwen.Qwen2Model",
	"AutoModelForCausalLM": "modeling_qwen.Qwen2ForCausalLM",
	"AutoModelForSequenceClassification": "modeling_qwen.Qwen2ForSequenceClassification"
	},
	"bos_token_id": 151643,
	"eos_token_id": 151643,
	"hidden_act": "silu",
	"hidden_size": 1536,
	"initializer_range": 0.02,
	"intermediate_size": 8960,
	"max_position_embeddings": 131072,
	"max_window_layers": 21,
	"model_type": "qwen2",
	"num_attention_heads": 12,
	"num_hidden_layers": 28,
	"num_key_value_heads": 2,
	"rms_norm_eps": 1e-06,
	"rope_theta": 1000000.0,
	"sliding_window": 131072,
	"tie_word_embeddings": false,
	"torch_dtype": "float32",
	"transformers_version": "4.41.2",
	"use_cache": true,
	"use_sliding_window": false,
	"vocab_size": 151646
	}