Sentence Similarity
Transformers
GGUF
sentence-transformers
English
Chinese
mteb
Qwen2-VL
vidore
imatrix
conversational
Instructions to use mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF", dtype="auto") - sentence-transformers
How to use mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mradermacher/gme-Qwen2-VL-7B-Instruct-i1-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] - llama-cpp-python
How to use mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF", filename="gme-Qwen2-VL-7B-Instruct.i1-IQ1_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 mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/gme-Qwen2-VL-7B-Instruct-i1-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 mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/gme-Qwen2-VL-7B-Instruct-i1-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 mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/gme-Qwen2-VL-7B-Instruct-i1-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 mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/gme-Qwen2-VL-7B-Instruct-i1-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 mradermacher/gme-Qwen2-VL-7B-Instruct-i1-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 mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF to start chatting
- Docker Model Runner
How to use mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/gme-Qwen2-VL-7B-Instruct-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gme-Qwen2-VL-7B-Instruct-i1-GGUF-Q4_K_M
List all available models
lemonade list
Ctrl+K
- 3.33 kB
- 6.21 kB
- 2.04 GB xet
- 1.9 GB xet
- 2.78 GB xet
- 2.6 GB xet
- 2.47 GB xet
- 2.27 GB xet
- 3.57 GB xet
- 3.5 GB xet
- 3.35 GB xet
- 3.11 GB xet
- 4.44 GB xet
- 4.22 GB xet
- 3.02 GB xet
- 2.83 GB xet
- 4.09 GB xet
- 3.81 GB xet
- 3.49 GB xet
- 4.44 GB xet
- 4.87 GB xet
- 4.68 GB xet
- 4.46 GB xet
- 5.44 GB xet
- 5.32 GB xet
- 6.25 GB xet