Instructions to use rozek/LLaMA-2-7B-32K_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 rozek/LLaMA-2-7B-32K_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 rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf rozek/LLaMA-2-7B-32K_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 rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf rozek/LLaMA-2-7B-32K_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 rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf rozek/LLaMA-2-7B-32K_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 rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M
Use Docker
docker model run hf.co/rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use rozek/LLaMA-2-7B-32K_GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rozek/LLaMA-2-7B-32K_GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rozek/LLaMA-2-7B-32K_GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M
- Ollama
How to use rozek/LLaMA-2-7B-32K_GGUF with Ollama:
ollama run hf.co/rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use rozek/LLaMA-2-7B-32K_GGUF with Docker Model Runner:
docker model run hf.co/rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M
- Lemonade
How to use rozek/LLaMA-2-7B-32K_GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull rozek/LLaMA-2-7B-32K_GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LLaMA-2-7B-32K_GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Output is only colons and dashes
When I try to display anything at all from the model its only dashes and dots. Any way to fix it. Im using rope-freq-scale set to 0.125 for ctx of 32k.
Hello! Sorry for the late response, but I have been quite busy in the last few days.
Which version of lama.cpp are you using? The Python version seems to have some issues - use the cpp version instead. Additionally, you may run into problems like the one you mentioned if you do not have enough memory where you plan to run the inference (i.e., CPU or GPU)
Im using python version itself and im running it on colab. Colab offers 15gb vram t4 gpu would that be an issue. Ill try changing to llama.cpp and test it. Thx for the response
good success!
@BlahBlah1 which one did you use? I am using Llama 2 chat 7B, but 4k context window is not enough for me...
@reddiamond what is your use case? cause you can try chunking it down to the models context window and repeating that process until the end.
@BlahBlah1 I am using LLaMA for graph querying. So it must go over all nodes and relations... I tested 32k model on my RAG app which works very good with normal q4 model but my output was '''''''''. I tested both, old model and new model with 32k context, on Llama.cpp and Llama-cpp-python examples and there was problem only with 32k model... It did not generate any response. So there must be a problem with library.