Editing Models with Task Arithmetic
Paper • 2212.04089 • Published • 9
How to use QuantFactory/Ph3task3-14B-GGUF with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Ph3task3-14B-GGUF", device_map="auto")How to use QuantFactory/Ph3task3-14B-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Ph3task3-14B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Ph3task3-14B-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Ph3task3-14B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Ph3task3-14B-GGUF:Q4_K_M
# 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 QuantFactory/Ph3task3-14B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Ph3task3-14B-GGUF:Q4_K_M
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 QuantFactory/Ph3task3-14B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Ph3task3-14B-GGUF:Q4_K_M
docker model run hf.co/QuantFactory/Ph3task3-14B-GGUF:Q4_K_M
How to use QuantFactory/Ph3task3-14B-GGUF with Ollama:
ollama run hf.co/QuantFactory/Ph3task3-14B-GGUF:Q4_K_M
How to use QuantFactory/Ph3task3-14B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Ph3task3-14B-GGUF:Q4_K_M
How to use QuantFactory/Ph3task3-14B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Ph3task3-14B-GGUF:Q4_K_M
lemonade run user.Ph3task3-14B-GGUF-Q4_K_M
lemonade list
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Ph3task3-14B-GGUF:# Run inference directly in the terminal:
llama cli -hf QuantFactory/Ph3task3-14B-GGUF:# 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 QuantFactory/Ph3task3-14B-GGUF:# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Ph3task3-14B-GGUF: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 QuantFactory/Ph3task3-14B-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Ph3task3-14B-GGUF:docker model run hf.co/QuantFactory/Ph3task3-14B-GGUF:This is quantized version of allknowingroger/Ph3task3-14B created using llama.cpp
This is a merge of pre-trained language models created using mergekit.
This model was merged using the task arithmetic merge method using jpacifico/Chocolatine-14B-Instruct-DPO-v1.2 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: jpacifico/Chocolatine-14B-Instruct-DPO-v1.2
parameters:
weight: 1.0
- model: jpacifico/Chocolatine-14B-Instruct-4k-DPO
parameters:
weight: 1.0
merge_method: task_arithmetic
base_model: jpacifico/Chocolatine-14B-Instruct-DPO-v1.2
parameters:
normalize: true
dtype: float16
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Ph3task3-14B-GGUF:# Run inference directly in the terminal: llama cli -hf QuantFactory/Ph3task3-14B-GGUF: