Instructions to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-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-Dolfin-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-Dolfin-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/ArliAI-Llama-3-8B-Dolfin-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-Dolfin-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/ArliAI-Llama-3-8B-Dolfin-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-Dolfin-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Dolfin-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-Dolfin-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-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-Dolfin-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-Dolfin-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-Dolfin-v1.0-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF with Ollama:
ollama run hf.co/QuantFactory/ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
QuantFactory/ArliAI-Llama-3-8B-Dolfin-v1.0-GGUF
This is quantized version of OwenArli/ArliAI-Llama-3-8B-Dolfin-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
Base model: https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
SFT fine tune of Meta Llama 3 8B Instruct Abliterated v3 by Failspy using an improved Dolphin and WizardLM dataset intended to remove GPT-isms and make the model follow instructions more exactly while paying attention to details better.
Since it is based on the Abliterated version of Llama 3 8B Instruct it should naturally not refuse to answer in the first place and this fine tuning should make it comply even better.
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 2.5 days on an RTX 4090
- 1 epoch training with a massive dataset for minimized repetition sickness.
- Using 4-bit loading and Qlora 64-rank 64-alpha resulting in ~2% trainable weights.
Llama 3 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-Dolfin-v1.0-GGUF
Base model
OwenArli/ArliAI-Llama-3-8B-Dolfin-v1.0