Instructions to use asedmammad/PersianMind-v1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use asedmammad/PersianMind-v1.0-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf asedmammad/PersianMind-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf asedmammad/PersianMind-v1.0-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf asedmammad/PersianMind-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf asedmammad/PersianMind-v1.0-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 asedmammad/PersianMind-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf asedmammad/PersianMind-v1.0-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 asedmammad/PersianMind-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf asedmammad/PersianMind-v1.0-GGUF:Q4_K_M
Use Docker
docker model run hf.co/asedmammad/PersianMind-v1.0-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use asedmammad/PersianMind-v1.0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "asedmammad/PersianMind-v1.0-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "asedmammad/PersianMind-v1.0-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/asedmammad/PersianMind-v1.0-GGUF:Q4_K_M
- Ollama
How to use asedmammad/PersianMind-v1.0-GGUF with Ollama:
ollama run hf.co/asedmammad/PersianMind-v1.0-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use asedmammad/PersianMind-v1.0-GGUF with Docker Model Runner:
docker model run hf.co/asedmammad/PersianMind-v1.0-GGUF:Q4_K_M
- Lemonade
How to use asedmammad/PersianMind-v1.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull asedmammad/PersianMind-v1.0-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.PersianMind-v1.0-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
UniversityOfTehran's PersianMind-v1.0 GGUF
These files are GGUF format model files for UniversityOfTehran's PersianMind-v1.0.
GGUF files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:
How to run in llama.cpp
I use the following command line, adjust for your tastes and needs:
./main -t 2 -ngl 32 -m PersianMind-v1.0.q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.2 -n -1 -e -p "This is a conversation with PersianMind. It is an artificial intelligence model designed by a team of NLP experts at the University of Tehran to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.\nYou: در مورد هوش مصنوعی توضیح بده.\nPersianMind: "
Change -t 2 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8.
Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins, you can use --interactive-first to start in interactive mode.
Though the model outputs good persian text among the open source models, enabling sampling or using high temperature values in llama.cpp causes the model to output non sense persian text, so currently I am using it with low temperature value with alpaca instruct template:
./main -t 2 -ngl 32 -m PersianMind-v1.0.q4_K_M.gguf --color -c 2048 --temp 0.2 --repeat_penalty 1.2 -n -1 -e -p "### Instruction: در مورد زنبور عسل توضیح بده ### Response:"
Compatibility
I have uploded both the original llama.cpp quant methods (q4_0, q4_1, q5_0, q5_1, q8_0) as well as the k-quant methods (q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K).
Please refer to llama.cpp and TheBloke's GGUF models for further explanation.
How to run in text-generation-webui
Further instructions here: text-generation-webui/docs/llama.cpp-models.md.
Thanks
Thanks to Pedram Rostami, Ali Salemi, and Mohammad Javad Dousti for providing checkpoints of the model.
Thanks to Georgi Gerganov and all of the awesome people in the AI community.
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