Resolving Interference When Merging Models
Paper • 2306.01708 • Published • 19
How to use QuantFactory/L3-OVA-Test-8B-GGUF with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/L3-OVA-Test-8B-GGUF", device_map="auto")How to use QuantFactory/L3-OVA-Test-8B-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/L3-OVA-Test-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/L3-OVA-Test-8B-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/L3-OVA-Test-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/L3-OVA-Test-8B-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/L3-OVA-Test-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M
docker model run hf.co/QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M
How to use QuantFactory/L3-OVA-Test-8B-GGUF with Ollama:
ollama run hf.co/QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M
How to use QuantFactory/L3-OVA-Test-8B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M
How to use QuantFactory/L3-OVA-Test-8B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M
lemonade run user.L3-OVA-Test-8B-GGUF-Q4_K_M
lemonade list
This is quantized version of Casual-Autopsy/L3-OVA-Test-8B created using llama.cpp
This is a merge of pre-trained language models created using mergekit.
This model was merged using the TIES merge method using meta-llama/Meta-Llama-3-8B-Instruct as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: meta-llama/Meta-Llama-3-8B-Instruct
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
density: 0.5
weight: 0.5
- model: ChaoticNeutrals/Templar_v1_8B
parameters:
density: 0.75
weight: [0.25, 0.0625, 0.0625, 0.0625, 0.0625]
- model: ChaoticNeutrals/Hathor_RP-v.01-L3-8B
parameters:
density: 0.75
weight: [0.0625, 0.25, 0.0625, 0.0625, 0.0625]
- model: Sao10K/L3-8B-Stheno-v3.2
parameters:
density: 0.75
weight: [0.0625, 0.0625, 0.25, 0.0625, 0.0625]
- model: ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B
parameters:
density: 0.75
weight: [0.0625, 0.0625, 0.0625, 0.25, 0.0625]
- model: ChaoticNeutrals/Domain-Fusion-L3-8B
parameters:
density: 0.75
weight: [0.0625, 0.0625, 0.0625, 0.0625, 0.25]
- model: ChaoticNeutrals/Templar_v1_8B
parameters:
density: 0.25
weight: [-0.125, -0.125, -0.125, -0.125, -0.5]
- model: ChaoticNeutrals/Hathor_RP-v.01-L3-8B
parameters:
density: 0.25
weight: [-0.125, -0.125, -0.5, -0.125, -0.125]
- model: Sao10K/L3-8B-Stheno-v3.2
parameters:
density: 0.25
weight: [-0.125, -0.5, -0.125, -0.125, -0.125]
- model: ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B
parameters:
density: 0.25
weight: [-0.125, -0.125, -0.125, -0.5, -0.125]
- model: ChaoticNeutrals/Domain-Fusion-L3-8B
parameters:
density: 0.25
weight: [-0.5, -0.125, -0.125, -0.125, -0.125]
merge_method: ties
base_model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
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