Instructions to use Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF", dtype="auto") - llama-cpp-python
How to use Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF", filename="FP8.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 Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-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 Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF # Run inference directly in the terminal: llama-cli -hf Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF # Run inference directly in the terminal: llama-cli -hf Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF
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 Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF # Run inference directly in the terminal: ./llama-cli -hf Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF
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 Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF
Use Docker
docker model run hf.co/Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF
- LM Studio
- Jan
- Ollama
How to use Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF with Ollama:
ollama run hf.co/Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF
- Unsloth Studio new
How to use Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-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 Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-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 Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-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 Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF to start chatting
- Docker Model Runner
How to use Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF
- Lemonade
How to use Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF
Run and chat with the model
lemonade run user.hawky-ai-Qwen2-Math-72B-Instruct-GGUF-{{QUANT_TAG}}List all available models
lemonade list
Sri-Vigneshwar-DJ/sarvam-2b-v0.5-GGUF
This model was converted to GGUF format from Qwen/Qwen2-72B-Instruct using llama.cpp
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) from https://github.com/ggerganov/llama.cpp.git
brew install llama.cpp or !git clone https://github.com/ggerganov/llama.cpp.git
Invoke the llama.cpp server or the CLI.
CLI:
! /content/llama.cpp/llama-cli -m ./sarvam-2b-v0.5-GGUF -n 90 --repeat_penalty 1.0 --color -i -r "User:" -f /content/llama.cpp/prompts/chat-with-bob.txt
or
llama-cli --hf-repo Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF --hf-file FP8.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Sri-Vigneshwar-DJ/sarvam-2b-v0.5-GGUF --hf-file FP8.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 or ''!make GGML_OPENBLAS=1' along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
or
!make GGML_OPENBLAS=1
Step 3: Run inference through the main binary.
./llama-cli --hf-repo Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF --hf-file FP8.gguf -p "FB_POST {poster} = ~Price?, Just give the upper and lower bound"
or
./llama-server --hf-repo Sri-Vigneshwar-DJ/hawky-ai-Qwen2-Math-72B-Instruct-GGUF --hf-file sFP8.gguf -c 2048
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