Instructions to use TheBloke/Phind-CodeLlama-34B-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/Phind-CodeLlama-34B-v2-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheBloke/Phind-CodeLlama-34B-v2-GGUF", dtype="auto") - llama-cpp-python
How to use TheBloke/Phind-CodeLlama-34B-v2-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TheBloke/Phind-CodeLlama-34B-v2-GGUF", filename="phind-codellama-34b-v2.Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use TheBloke/Phind-CodeLlama-34B-v2-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 TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TheBloke/Phind-CodeLlama-34B-v2-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 TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TheBloke/Phind-CodeLlama-34B-v2-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 TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf TheBloke/Phind-CodeLlama-34B-v2-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 TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use TheBloke/Phind-CodeLlama-34B-v2-GGUF with Ollama:
ollama run hf.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M
- Unsloth Studio
How to use TheBloke/Phind-CodeLlama-34B-v2-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 TheBloke/Phind-CodeLlama-34B-v2-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 TheBloke/Phind-CodeLlama-34B-v2-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TheBloke/Phind-CodeLlama-34B-v2-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use TheBloke/Phind-CodeLlama-34B-v2-GGUF with Docker Model Runner:
docker model run hf.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M
- Lemonade
How to use TheBloke/Phind-CodeLlama-34B-v2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Phind-CodeLlama-34B-v2-GGUF-Q4_K_M
List all available models
lemonade list
Infinite repeating characters
I'm using Q5_K_M with langchain's llamacpp. After about 25 words the output infinite loops on either a phrase word or character (the next file the next file the next file the next file, set set set set set set set, pythonnnnnnnnnnnnnnnnnnnnn)
Doesn't seem to be effected by context length, top_p, top_k, temperature. The Q8_0 behaves the same. Anyone else having this issue?
Yep the same here with q3 and q4 ...