Instructions to use wolframko/bge-code-v1-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use wolframko/bge-code-v1-Q4_K_M-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("wolframko/bge-code-v1-Q4_K_M-GGUF") sentences = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use wolframko/bge-code-v1-Q4_K_M-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wolframko/bge-code-v1-Q4_K_M-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use wolframko/bge-code-v1-Q4_K_M-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf wolframko/bge-code-v1-Q4_K_M-GGUF:Q4_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf wolframko/bge-code-v1-Q4_K_M-GGUF:Q4_K_M --embeddings
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible embeddings server: ./llama-server -hf wolframko/bge-code-v1-Q4_K_M-GGUF:Q4_K_M --embeddings
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 embeddings server: ./build/bin/llama-server -hf wolframko/bge-code-v1-Q4_K_M-GGUF:Q4_K_M --embeddings
- LM Studio
- Jan
- Ollama
How to use wolframko/bge-code-v1-Q4_K_M-GGUF with Ollama:
ollama run hf.co/wolframko/bge-code-v1-Q4_K_M-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use wolframko/bge-code-v1-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/wolframko/bge-code-v1-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use wolframko/bge-code-v1-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull wolframko/bge-code-v1-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.bge-code-v1-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
wolframko/bge-code-v1-Q4_K_M-GGUF
This model was converted to GGUF format from BAAI/bge-code-v1 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 wolframko/bge-code-v1-Q4_K_M-GGUF --hf-file bge-code-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo wolframko/bge-code-v1-Q4_K_M-GGUF --hf-file bge-code-v1-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 wolframko/bge-code-v1-Q4_K_M-GGUF --hf-file bge-code-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo wolframko/bge-code-v1-Q4_K_M-GGUF --hf-file bge-code-v1-q4_k_m.gguf -c 2048
- Downloads last month
- 20
4-bit
Model tree for wolframko/bge-code-v1-Q4_K_M-GGUF
Base model
BAAI/bge-code-v1