Instructions to use hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF", filename="deepseek-coder-6.7b-instruct-iq4_nl-imat.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: llama-cli -hf hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: llama-cli -hf hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL
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 hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL
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 hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL
Use Docker
docker model run hf.co/hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL
- LM Studio
- Jan
- Ollama
How to use hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF with Ollama:
ollama run hf.co/hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL
- Unsloth Studio new
How to use hellork/deepseek-coder-6.7b-instruct-IQ4_NL-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 hellork/deepseek-coder-6.7b-instruct-IQ4_NL-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 hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF to start chatting
- Docker Model Runner
How to use hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF with Docker Model Runner:
docker model run hf.co/hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL
- Lemonade
How to use hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF:IQ4_NL
Run and chat with the model
lemonade run user.deepseek-coder-6.7b-instruct-IQ4_NL-GGUF-IQ4_NL
List all available models
lemonade list
hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF
This model was converted to GGUF format from deepseek-ai/deepseek-coder-6.7b-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 hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF --hf-file deepseek-coder-6.7b-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF --hf-file deepseek-coder-6.7b-instruct-iq4_nl-imat.gguf -c 2048
The Ship's Computer:
Interact with this model by speaking to it. Lean, fast, & private, networked speech to text, AI images, multi-modal voice chat, control apps, webcam, and sound with less than 4GiB of VRAM. whisper_dictation
Quick start
git clone -b main --single-branch https://github.com/themanyone/whisper_dictation.git
pip install -r whisper_dictation/requirements.txt
git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
GGML_CUDA=1 make -j # assuming CUDA is available. see docs
ln -s server ~/.local/bin/whisper_cpp_server # (just put it somewhere in $PATH)
whisper_cpp_server -l en -m models/ggml-tiny.en.bin --port 7777
# -ngl option assumes CUDA or othr AI acceleration is available. see docs
llama-server --hf-repo hellork/calme-2.1-qwen2-7b-IQ4_NL-GGUF --hf-file calme-2.1-qwen2-7b-iq4_nl-imat.gguf -c 2048 -ngl 17 --port 8888
cd whisper_dictation
./whisper_cpp_client.py
Install llama.cpp via git:
Step 1: 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 hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF --hf-file deepseek-coder-6.7b-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF --hf-file deepseek-coder-6.7b-instruct-iq4_nl-imat.gguf -c 2048
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deepseek-ai/deepseek-coder-6.7b-instruct