Instructions to use QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-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 QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-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 QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-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 QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-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 QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF with Ollama:
ollama run hf.co/QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF
This is quantized version of OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0 created using llama.cpp
Model Description
Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
This is release v1.0 of Awanllm Cumulus series of models that aim to be uncensored and have zero refusals and zero warnings.
This model should be good for general use cases as the OG Llama 3 8B model but it should be especially better for story writing or RP use cases.
It is the most uncensored yet, thanks to using https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 as the base model.
In terms of reasoning and intelligence, this model is probably a bit worse than the OG Meta Llama 3 8B Instruct because of the decensoring. However we believe it is worth it for the decensoring, as even with jailbreak prompts Llama 3 8B Instruct will never get remotely close to this model.
Best practices:
- Be precise and explain what you want the model to do. It has less base "personality" than the OG model but it will act however you tell it to.
- This model works best with system prompts that tells it that it is the character, instead of telling it to act as a character.
Training:
- Full 8192 sequence length.
- Training duration is around 4 days on an RTX 4090, using 4-bit loading and Qlora 64-rank 64-alpha resulting in ~2% trainable weights.
Instruct format:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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Model tree for QuantFactory/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF
Base model
OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0