flammenai/casual-conversation-DPO
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How to use QuantFactory/llama3.1-cc-8B-GGUF with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/llama3.1-cc-8B-GGUF", device_map="auto")How to use QuantFactory/llama3.1-cc-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/llama3.1-cc-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/llama3.1-cc-8B-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/llama3.1-cc-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/llama3.1-cc-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/llama3.1-cc-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/llama3.1-cc-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/llama3.1-cc-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/llama3.1-cc-8B-GGUF:Q4_K_M
docker model run hf.co/QuantFactory/llama3.1-cc-8B-GGUF:Q4_K_M
How to use QuantFactory/llama3.1-cc-8B-GGUF with Ollama:
ollama run hf.co/QuantFactory/llama3.1-cc-8B-GGUF:Q4_K_M
How to use QuantFactory/llama3.1-cc-8B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/llama3.1-cc-8B-GGUF:Q4_K_M
How to use QuantFactory/llama3.1-cc-8B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/llama3.1-cc-8B-GGUF:Q4_K_M
lemonade run user.llama3.1-cc-8B-GGUF-Q4_K_M
lemonade list
This is quantized version of nbeerbower/llama3.1-cc-8B created using llama.cpp
mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated finetuned on flammenai/casual-conversation-DPO.
This is an experimental finetune that formats the conversation data sequentially with the Llama 3 template.
Finetuned using an A100 on Google Colab for 3 epochs.
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Base model
meta-llama/Llama-3.1-8B