Instructions to use ikawrakow/llama-v1-2bit-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ikawrakow/llama-v1-2bit-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ikawrakow/llama-v1-2bit-gguf", filename="llama-v1-13b-q2k.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 ikawrakow/llama-v1-2bit-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ikawrakow/llama-v1-2bit-gguf # Run inference directly in the terminal: llama-cli -hf ikawrakow/llama-v1-2bit-gguf
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ikawrakow/llama-v1-2bit-gguf # Run inference directly in the terminal: llama-cli -hf ikawrakow/llama-v1-2bit-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 ikawrakow/llama-v1-2bit-gguf # Run inference directly in the terminal: ./llama-cli -hf ikawrakow/llama-v1-2bit-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 ikawrakow/llama-v1-2bit-gguf # Run inference directly in the terminal: ./build/bin/llama-cli -hf ikawrakow/llama-v1-2bit-gguf
Use Docker
docker model run hf.co/ikawrakow/llama-v1-2bit-gguf
- LM Studio
- Jan
- Ollama
How to use ikawrakow/llama-v1-2bit-gguf with Ollama:
ollama run hf.co/ikawrakow/llama-v1-2bit-gguf
- Unsloth Studio
How to use ikawrakow/llama-v1-2bit-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 ikawrakow/llama-v1-2bit-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 ikawrakow/llama-v1-2bit-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ikawrakow/llama-v1-2bit-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ikawrakow/llama-v1-2bit-gguf with Docker Model Runner:
docker model run hf.co/ikawrakow/llama-v1-2bit-gguf
- Lemonade
How to use ikawrakow/llama-v1-2bit-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ikawrakow/llama-v1-2bit-gguf
Run and chat with the model
lemonade run user.llama-v1-2bit-gguf-{{QUANT_TAG}}List all available models
lemonade list
This repository contains 2-bit quantized LLaMA-v1 models in GGUF format for use with llama.cpp.
All tensors are quantized with Q2_K, except for output.weight, which is Q6_K, and, in the case of LLaMA-v2-70B, attn_v, which is Q4_K.
The quantized models differ from the standard llama.cpp 2-bit quantization in two ways:
- These are actual 2-bit quantized models instead of the mostly 3-bit quantization provided by the standard
llama.cppQ2_Kquantization method - The models were prepared with a refined (but not yet published) k-quants quantization approach
The table shows Wikitext perplexities for a context length of 2048 tokens computed with these models using llama.cpp
| Model | Perplexity |
|---|---|
| 7B | 6.4023 |
| 13B | 5.3967 |
| 30B | 4.5065 |
| 65B | 3.9136 |
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We're not able to determine the quantization variants.